Metadata-Version: 2.4
Name: agentkit-cli
Version: 0.84.0
Summary: Unified CLI for the Agent Quality Toolkit (agentmd, coderace, agentlint, agentreflect)
Project-URL: Homepage, https://github.com/mikiships/agentkit-cli
Project-URL: Repository, https://github.com/mikiships/agentkit-cli
Project-URL: Changelog, https://github.com/mikiships/agentkit-cli/blob/main/CHANGELOG.md
Author-email: mikiships <frank.michaels2123@gmail.com>
License: MIT
License-File: LICENSE
Keywords: agents,ai,benchmark,cli,llm,quality,toolkit
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Topic :: Software Development :: Testing
Requires-Python: >=3.11
Requires-Dist: pyyaml>=6.0.0
Requires-Dist: rich>=13.0.0
Requires-Dist: typer>=0.9.0
Requires-Dist: watchdog>=3.0.0
Description-Content-Type: text/markdown

# agentkit-cli

[![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-blue?logo=github)](https://mikiships.github.io/agentkit-cli/)
[![PyPI](https://img.shields.io/pypi/v/agentkit-cli)](https://pypi.org/project/agentkit-cli/)

Unified CLI for the Agent Quality Toolkit (agentmd, coderace, agentlint, agentreflect).

## Installation

```bash
pip install agentkit-cli
```

## Quick Start

```bash
pip install agentkit-cli
agentkit quickstart    # 🚀 fastest path to a score — start here
```

`agentkit quickstart` checks your toolchain, runs a fast composite score (agentlint + agentmd), prints a beautiful Rich summary, and optionally publishes a shareable score card — all in under 60 seconds.

```bash
agentkit run           # run the full pipeline
agentkit score         # compute composite score
agentkit gate          # fail if score < threshold
agentkit org github:vercel   # score every public repo in a GitHub org
agentkit ecosystem     # 🌐 "State of AI Agent Readiness" — which language ecosystem is most AI-agent-ready?
```

**State of Ecosystem:** `agentkit ecosystem` runs a macro scan across major language/tech ecosystems (Python, TypeScript, Rust, Go, Java, and more) and produces a ranked "State of AI Agent Readiness" report. Share it with `--share` or save to HTML with `--output report.html`.

## Demo

<!-- demo.gif placeholder — generate with: agentkit demo --record && vhs demo.tape -->

Record a terminal demo with [VHS](https://github.com/charmbracelet/vhs):

```bash
agentkit demo --record    # generates demo.tape
vhs demo.tape             # renders demo.gif
```

The tape records the full quickstart → run → benchmark flow.

## Configuration

agentkit uses `.agentkit.toml` for project-level configuration.

```bash
agentkit config init       # create .agentkit.toml with defaults
agentkit config show       # show effective config with sources
agentkit config set gate.min_score 80
agentkit config get gate.min_score
```

### Config Precedence

`CLI flags > env vars > project .agentkit.toml > user config > defaults`

## Profiles

Profiles are named presets for gate thresholds, notify config, and sweep targets.
Switch your entire quality policy in one command.

### Built-in Presets

| Profile | Min Score | Max Drop | Notify On | Gate |
|---------|-----------|----------|-----------|------|
| `strict` | 85 | 3 | fail | enabled |
| `balanced` | 70 | 10 | never | enabled |
| `minimal` | 50 | 20 | never | disabled |

### Usage

```bash
# Switch to strict quality standards
agentkit profile use strict

# List all profiles (built-in + user-defined)
agentkit profile list

# Show profile details
agentkit profile show strict

# Run gate with a specific profile
agentkit gate --profile strict

# Create a custom profile based on strict
agentkit profile create myprofile --from strict --min-score 90

# Export a profile as JSON or TOML
agentkit profile export strict --format json
```

### Using Profiles with Commands

All major commands support `--profile`:

```bash
agentkit gate --profile strict
agentkit run --profile balanced
agentkit sweep --profile minimal owner/repo1 owner/repo2
agentkit score --profile balanced
agentkit analyze --profile strict github:owner/repo
```

Explicit CLI flags always override profile values:
```bash
# Uses strict profile but overrides min-score to 99
agentkit gate --profile strict --min-score 99
```

## `agentkit llmstxt` — AI-Accessible Documentation

[llms.txt](https://llmstxt.org/) is a standard that tells LLMs how to consume a project's documentation and API surface — making your repo accessible to AI-powered tools beyond just coding agents.

```bash
# Generate llms.txt for current directory
agentkit llmstxt

# Generate both llms.txt and llms-full.txt (with inline content)
agentkit llmstxt --full --output ./dist/

# Analyze a GitHub repo
agentkit llmstxt github:tiangolo/fastapi --json

# Validate an existing llms.txt
agentkit llmstxt --validate

# Get quality score
agentkit llmstxt --score --json
```

Sample `llms.txt` output:
```
# my-project v1.2.0

> A fast, lightweight library for building AI agents.

## Docs

- [README](README.md): Project overview and getting started guide.
- [Changelog](CHANGELOG.md): Version history and release notes.
- [Guide](docs/guide.md)

## API

- [my-project API](my_project/__init__.py): Main API module.

## Examples

- [Basic Example](examples/basic.md)
```

**Integration with existing commands:**
```bash
# Generate llms.txt as part of standard run pipeline
agentkit run --llmstxt

# Include llms.txt card in HTML report
agentkit report --llmstxt
```

## `agentkit migrate` — Convert Between AI Agent Context Formats

Developers using Claude Code, Codex, and Gemini CLI each expect different context file formats (`CLAUDE.md`, `AGENTS.md`, `llms.txt`). `agentkit migrate` converts between them automatically.

| Source | Target | Notes |
|--------|--------|-------|
| `AGENTS.md` | `CLAUDE.md` | Operational rules → project-focused format |
| `AGENTS.md` | `llms.txt` | Operational rules → llmstxt.org format |
| `CLAUDE.md` | `AGENTS.md` | Project context → operational format |
| `CLAUDE.md` | `llms.txt` | Project context → llmstxt.org format |
| `llms.txt` | `CLAUDE.md` | AI-accessible docs → CLAUDE.md |
| `llms.txt` | `AGENTS.md` | AI-accessible docs → AGENTS.md |

```bash
agentkit migrate             # auto-detect source, generate all formats
agentkit migrate --all --force
agentkit migrate --from agents-md --to claude-md
agentkit migrate --dry-run
agentkit sync --check        # exit 1 if stale
agentkit sync --fix          # re-generate stale files
agentkit llmstxt --sync-from agents-md
agentkit run --migrate       # generate missing formats before analysis
```

## Commands

- `agentkit quickstart` — 🚀 fastest path to a score (start here)
- `agentkit run` — run the full pipeline
- `agentkit gist` — publish analysis output as a permanent GitHub Gist (use `--from <file>`, `--public`, `--description`)
- `agentkit score` — compute composite score
- `agentkit gate` — fail if score < threshold
- `agentkit redteam [PATH]` — adversarial eval: score how well your agent context resists attacks
- `agentkit analyze <target>` — analyze any GitHub repo
- `agentkit sweep <targets>` — batch analyze multiple repos
- `agentkit duel <repo1> <repo2>` — head-to-head agent-readiness comparison
- `agentkit daily-duel` — 🗓️ zero-input daily repo duel (auto-selects contrasting pairs, generates tweet-ready text)
- `agentkit hot` — 🔥 score GitHub's daily trending repos for agent-readiness, output the most surprising finding as a tweet
- `agentkit topic <topic>` — rank top GitHub repos for a topic by agent-readiness (e.g. `python`, `llm`, `agents`)
- `agentkit topic-duel <topic1> <topic2>` — head-to-head agent-readiness comparison of two GitHub topics (e.g. `fastapi` vs `django`)
- `agentkit topic-league <topic1> <topic2> ... <topicN>` — multi-topic standings comparison for 2–10 GitHub topics (e.g. `python rust go typescript`)
- `agentkit ecosystem` — macro "State of AI Agent Readiness" scan across major language ecosystems (default: python, typescript, rust, go, java); supports `--preset extended` for 12 ecosystems, `--preset custom --topics "..."` for user-defined sets
- `agentkit user-rank <topic>` — rank top GitHub contributors for a topic by agent-readiness (e.g. `python`, `rust`, `llm`)
- `agentkit user-duel github:<user1> github:<user2>` — head-to-head agent-readiness comparison between two GitHub developers
- `agentkit user-tournament github:<u1> github:<u2> [github:<uN>...]` — bracket-style agent-readiness tournament for N GitHub developers
- `agentkit tournament <repo1> ... <repoN>` — round-robin bracket across 4-16 repos
- `agentkit profile <sub>` — manage quality profiles
- `agentkit config <sub>` — manage configuration
- `agentkit history` — show score history
- `agentkit timeline` — visual quality timeline (HTML chart from history DB)
- `agentkit leaderboard` — compare runs by label
- `agentkit insights` — cross-repo pattern synthesis
- `agentkit trending` — fetch and rank trending GitHub repos by agent quality
- `agentkit daily` — generate a daily leaderboard of the most agent-ready GitHub repos
- `agentkit pages-trending` — fetch trending repos, score for agent-readiness, publish daily leaderboard to GitHub Pages
- `agentkit org <owner>` — score every public repo in a GitHub org or user account
- `agentkit pr github:<owner>/<repo>` — submit a CLAUDE.md PR to any public GitHub repo
- `agentkit campaign <target>` — batch PR submission to multiple repos in one command
- `agentkit search [query]` — discover GitHub repos missing CLAUDE.md / AGENTS.md

## Search: Discover Repos Missing Context Files

`agentkit search` discovers GitHub repos that are missing CLAUDE.md or AGENTS.md — the best targets for `agentkit campaign`.

```bash
# Find Python AI-agent repos without context files
agentkit search "ai agents" --language python --missing-only

# Filter by topic and minimum stars
agentkit search --topic ai-agents --min-stars 500 --limit 30

# Export as JSON (pipe to agentkit campaign targets)
agentkit search "llm tools" --missing-only --json > targets.json

# Generate a shareable HTML report
agentkit search "coding agents" --output report.html --share

# Full campaign flywheel: search → campaign
agentkit campaign --from-search "ai agents" --language python --min-stars 500
```

The search result table shows each repo's star count, language, and whether CLAUDE.md or AGENTS.md is present.

## Campaign: Batch PR Submission

`agentkit campaign` finds repos missing CLAUDE.md and submits PRs to all of them in one command.

```bash
# Submit CLAUDE.md PRs to all public repos in an org (up to 5, default)
agentkit campaign github:pallets

# Discover repos without submitting PRs (dry run)
agentkit campaign github:pallets --dry-run --limit 10

# Target by topic
agentkit campaign topic:ai-agents --language python --min-stars 500

# Use a file of repos
agentkit campaign repos-file:my-targets.txt

# Only discover repos (no PRs)
agentkit campaign github:pallets --skip-pr

# Generate and share an HTML report
agentkit campaign github:pallets --share
```

Example output:
```
Campaign ID: abc12345
Target: github:pallets  Limit: 5  File: CLAUDE.md

┌────────────┬────────┬────────┬────────────────────────────────┐
│ Repo       │ Stars  │ Status │ PR URL / Note                  │
├────────────┼────────┼────────┼────────────────────────────────┤
│ flask      │ ★ 68k  │ ✅ PR  │ https://github.com/.../pull/42 │
│ click      │ ★ 15k  │ ✅ PR  │ https://github.com/.../pull/7  │
│ jinja      │ ★ 10k  │ ⏭ skip│ Already has context file       │
│ werkzeug   │ ★ 7k   │ ✅ PR  │ https://github.com/.../pull/12 │
│ markupsafe │ ★ 600  │ ❌ err │ Fork creation failed           │
└────────────┴────────┴────────┴────────────────────────────────┘
Campaign complete. 3 PRs opened, 1 skipped, 1 failed.
```

Options:
- `--limit N` — max repos to target (default: 5)
- `--language TEXT` — filter by language (e.g. python, typescript)
- `--min-stars N` — minimum stars threshold (default: 100)
- `--file TEXT` — context file name (default: CLAUDE.md)
- `--force` — submit PR even if context file exists
- `--dry-run` — show what would happen, no PRs opened
- `--json` — output CampaignResult as JSON
- `--no-filter` — skip the "already has context file" check
- `--skip-pr` — only discover repos, don't submit PRs
- `--share` — upload HTML report to here.now

## agentkit track — Monitor Campaign PR Outcomes

After running `agentkit campaign`, use `agentkit track` to see which PRs got merged, closed, or are still open.

```bash
# Show last 20 tracked PRs
agentkit track

# Filter to a specific campaign
agentkit track --campaign-id abc12345

# Show all PRs (no limit)
agentkit track --all

# JSON output for CI/automation
agentkit track --json

# Upload a shareable HTML status report
agentkit track --share
```

Example output:
```
┌──────────────┬──────┬────────┬───────────┬─────────┬────────────┐
│ Repo         │ PR # │ Status │ Days Open │ Reviews │ Submitted  │
├──────────────┼──────┼────────┼───────────┼─────────┼────────────┤
│ pallets/flask│ 6001 │ merged │ 3         │ 2       │ 2026-03-14 │
│ encode/httpx │ 892  │ open   │ 1         │ 0       │ 2026-03-16 │
└──────────────┴──────┴────────┴───────────┴─────────┴────────────┘
2 merged, 1 open, 0 closed
```

Options:
- `--campaign-id TEXT` — filter to a specific campaign
- `--limit N` — max PRs to show (default: 20)
- `--all` — show all tracked PRs (no limit)
- `--json` — output structured JSON
- `--share` — upload dark-theme HTML report to here.now

View campaign history with:
```bash
agentkit history --campaigns
agentkit history --campaign-id <id>
```

## Org Analysis

`agentkit org` answers: "Which repos in this GitHub org are most AI-agent-ready?"

```bash
# Score all public repos in an org or user account
agentkit org github:vercel

# Include forked and archived repos, cap at 20
agentkit org github:microsoft --include-forks --include-archived --limit 20

# Parallel analysis with 5 workers, save HTML report
agentkit org github:anthropics --parallel 5 --output report.html

# Share report online
agentkit org github:openai --share

# JSON output for scripting
agentkit org github:tiangolo --json

# Use GitHub token to avoid rate limits
agentkit org github:google --token ghp_xxx

# Auto-generate CLAUDE.md for repos below 80 and show before/after score lift
agentkit org github:pallets --generate

# Only generate for repos scoring below 60, share an HTML before/after report
agentkit org github:pallets --generate --generate-only-below 60 --share
```

### `--generate` flag

`--generate` turns the audit from read-only to actionable: for every repo below the threshold (default: 80), it clones the repo locally, runs `agentmd generate` to create a CLAUDE.md, re-scores the repo, and shows the before/after lift.

```
Before: pallets/flask  28.6/F
After:  pallets/flask  91.4/A  (+62.8 pts)
```

All generation is done in temporary local clones — no remote writes to GitHub.

Options:
- `--generate-only-below N` — only generate for repos scoring below N (default: 80)
- `--share` with `--generate` — HTML report shows Before / After columns with color-coded delta badges

## Benchmark

`agentkit benchmark` answers: "Which AI agent — Claude, Codex, or Gemini — performs best on YOUR specific codebase?"

```bash
# Benchmark all three agents on current project (default tasks)
agentkit benchmark

# Custom agents and tasks
agentkit benchmark --agents claude,codex --tasks bug-hunt,refactor

# 3 rounds for statistical confidence
agentkit benchmark --rounds 3

# Export JSON results
agentkit benchmark --json > results.json

# Save HTML report
agentkit benchmark --output report.html

# Publish shareable dark-theme report
agentkit benchmark --share
```

The benchmark runs 5 built-in tasks (bug-hunt, refactor, concurrent-queue, api-client, context-use) against each agent via coderace and produces a ranked comparison table showing mean score, mean time, and win rate.

## Trending Analysis

`agentkit trending` answers: "Which repos blowing up on GitHub are most AI-agent-ready today?"

```bash
# Rank this week's trending AI repos (default)
agentkit trending

# Fast mode: list repos without scoring
agentkit trending --no-analyze

# Filter by topic, publish a shareable report
agentkit trending --topic ai-agent --share

# Weekly trending, top 15, min 500 stars, JSON output
agentkit trending --period week --limit 15 --min-stars 500 --json

# Use a GitHub token for higher rate limits
agentkit trending --token ghp_xxx
```

Output: a ranked Rich table (Rank | Repo | Stars | Score | Grade | URL) and optionally a dark-theme HTML report published to here.now.

## Daily Duel

`agentkit daily-duel` is a zero-input content generator: pick a daily contrasting repo pair, duel them, output tweet-ready text. Designed for automated social media flywheels.

```bash
# Show today's duel (auto-selects pair by date seed)
agentkit daily-duel

# Specify a custom seed for reproducibility
agentkit daily-duel --seed "2026-01-15"

# Override auto-pick with explicit repos
agentkit daily-duel --pair tiangolo/fastapi pallets/flask

# Include deep-dive redteam dimension
agentkit daily-duel --deep

# Publish HTML report and include URL in tweet
agentkit daily-duel --share

# Output only tweet text (for piping to scripts)
agentkit daily-duel --quiet

# JSON output for automation
agentkit daily-duel --json

# Preview 7-day schedule without running analysis
agentkit daily-duel --calendar

# Save HTML report to file
agentkit daily-duel --output report.html
```

Example output (terminal):

```
🗓️  Daily Duel — 2026-03-20
  tiangolo/fastapi vs pallets/flask  [web-frameworks]

  fastapi  B · 80.5
  flask    C · 65.2

⚔️  Repo Duel Dimensions
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ Dimension           ┃ fastapi ┃ Winner ┃  flask ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ Composite Score     │   80.5  │ fastapi│  65.2  │
│ Context Coverage    │   78.0  │ fastapi│  54.0  │
│ Test Coverage       │   85.0  │ fastapi│  62.0  │
│ Lint Score          │   79.0  │ fastapi│  71.0  │
└─────────────────────┴─────────┴────────┴────────┘

🏆 fastapi wins!  Grade B · 80.5

Tweet-ready
┌────────────────────────────────────────────────────────────────┐
│ tiangolo/fastapi vs pallets/flask agent-readiness: fastapi     │
│ 80/100 (B), flask 65/100 (C). Winner: fastapi on 3/4           │
│ dimensions.                                                      │
└────────────────────────────────────────────────────────────────┘
```

The result is also written to `~/.local/share/agentkit/daily-duel-latest.json` for consumption by automation systems (e.g., x-organic-posts cron).

## Trending Repos

`agentkit hot` fetches GitHub's daily trending repos, scores each for agent-readiness via `ExistingStateScorer`, and produces a tweet-ready observation about the most surprising finding.

```bash
# Score today's top 10 trending repos (all languages)
agentkit hot

# Filter by language
agentkit hot --language python

# Just the tweet text (for piping to frigatebird)
agentkit hot --tweet-only

# More repos
agentkit hot --limit 20

# Upload HTML report to here.now and include URL in tweet
agentkit hot --share

# Structured JSON output
agentkit hot --json
```

The script `scripts/post-hot.sh` automates the full post pipeline:

```bash
./scripts/post-hot.sh            # plain tweet
./scripts/post-hot.sh --share    # with here.now report URL
./scripts/post-hot.sh --dry-run  # print tweet, don't post
```

Logs are written to `~/.local/share/agentkit/hot-post-log.jsonl`.

## Spotlight Queue

`agentkit spotlight-queue` manages a rotation queue of repos for the spotlight cron, so `scripts/post-spotlight.sh` can fire daily without manual `--target` input.

```bash
# Seed with 10 default repos
agentkit spotlight-queue seed

# Add a repo
agentkit spotlight-queue add github:owner/repo

# See the full queue with last-spotlighted dates
agentkit spotlight-queue list

# Get the next repo to spotlight (plain text, for scripting)
agentkit spotlight-queue next

# Mark a repo as done (updates lastSpotlighted to today)
agentkit spotlight-queue mark-done github:owner/repo

# Remove a repo
agentkit spotlight-queue remove github:owner/repo

# Clear the queue
agentkit spotlight-queue clear
```

The queue is stored at `~/.local/share/agentkit/spotlight-queue.json`. On first use, it auto-seeds with 10 popular repos if the file does not exist.

**Rotation logic:** repos never spotlighted are returned first (in order added). Once all repos have been spotlighted, the one with the oldest `lastSpotlighted` date is returned next.

`scripts/post-spotlight.sh` uses the queue automatically when no `--target` is given:

```bash
# Uses spotlight-queue next to pick target, marks done after success
./scripts/post-spotlight.sh

# Explicit target (bypasses queue)
./scripts/post-spotlight.sh --target django/django

# Dry-run (print tweet text only, no post)
./scripts/post-spotlight.sh --dry-run
```

## Daily Leaderboard

`agentkit daily` is a content flywheel: run once/day, get a shareable ranked HTML report showing "Today's most AI-agent-ready repos."

```bash
# Show today's leaderboard in the terminal
agentkit daily

# Specify a date
agentkit daily --date 2026-03-19

# Publish to here.now and print the URL
agentkit daily --share

# Cron-friendly: output URL only (pipe into scripts or post to X)
agentkit daily --share --quiet

# Save HTML report to a file
agentkit daily --output daily-report.html

# JSON output
agentkit daily --json

# Filter by minimum score
agentkit daily --min-score 70
```

Example output (terminal):

```
agentkit daily — date: 2026-03-19, limit: 20

 Rank  Repo                      Stars   Score  Top Finding
 🥇    microsoft/autogen         30,000   91    Multi-agent framework with strong tool support
 🥈    openai/openai-python      25,000   88    Well-structured SDK with clear API surface
 🥉    anthropics/anthropic-sdk  12,000   85    Comprehensive documentation and type hints
 #4    langchain-ai/langchain     8,000   78    Agent/LLM keyword in description
```

### Permanent GitHub Pages URL

Use `--pages` to publish a permanent, auto-updating leaderboard to GitHub Pages:

```bash
# Publish to GitHub Pages (auto-detects repo from git remote)
agentkit daily --pages

# Target a specific repo
agentkit daily --pages --pages-repo github:owner/repo

# Override the output path (default: docs/leaderboard.html)
agentkit daily --pages --pages-path docs/leaderboard.html
```

On success, prints: `https://owner.github.io/repo/leaderboard.html`

If GitHub Pages publish fails, falls back to `--share` (here.now 24h link) automatically.

### GitHub Actions cron example

```yaml
# .github/workflows/examples/agentkit-daily-leaderboard-pages.yml
on:
  schedule:
    - cron: '0 8 * * *'  # 8 AM UTC daily
permissions:
  contents: write
  pages: write
jobs:
  publish-leaderboard:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
      - run: pip install agentkit-cli
      - run: agentkit daily --pages --pages-repo github:${{ github.repository }}
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
```

See full example: `.github/workflows/examples/agentkit-daily-leaderboard-pages.yml`

### Share (24h URL, default)

```bash
# Publish to here.now and print the URL (24h expiry)
agentkit daily --share

# Cron-friendly: output URL only
agentkit daily --share --quiet
```

Add to your GitHub Actions for automated daily publishing:

```yaml
- name: Run daily leaderboard
  run: |
    URL=$(agentkit daily --share --quiet)
    echo "url=$URL" >> "$GITHUB_OUTPUT"
```

See the full example: `.github/workflows/examples/agentkit-daily-leaderboard.yml`

## Tournament

`agentkit tournament` runs a round-robin bracket across 4-16 repos and ranks them by win/loss record with avg score tiebreak.

```bash
# Run a 4-repo tournament
agentkit tournament github:fastapi/fastapi github:tiangolo/starlette github:django/django github:pallets/flask

# Publish a shareable HTML bracket report
agentkit tournament github:fastapi/fastapi github:tiangolo/starlette github:django/django github:pallets/flask --share

# JSON output for CI/scripting
agentkit tournament github:fastapi/fastapi github:tiangolo/starlette github:django/django github:pallets/flask --json

# Sequential (no parallel), quiet mode, save HTML
agentkit tournament github:fastapi/fastapi github:tiangolo/starlette github:django/django github:pallets/flask \
  --no-parallel --quiet --output bracket.html
```

Output: standings table (Rank | Repo | W-L | Avg Score | Grade), match results matrix, and winner banner. Use `--share` to publish a dark-theme HTML bracket to here.now.

## Portfolio Insights

Once you've analyzed multiple repos with `agentkit analyze` or `agentkit run`, the
`agentkit insights` command synthesizes patterns across all historical runs:

```bash
# Portfolio health summary (avg score, best/worst repo, top issue)
agentkit insights

# Most common agentlint findings across all repos
agentkit insights --common-findings

# Repos scoring in the bottom quartile
agentkit insights --outliers

# Repos with significant score movement between runs
agentkit insights --trending

# All sections in one view
agentkit insights --all

# Machine-readable JSON (useful for scripts/dashboards)
agentkit insights --json

# Use a specific history DB
agentkit insights --db /path/to/history.db
```

Store agentlint findings alongside scores for richer cross-repo analysis:

```bash
agentkit run --record-findings
agentkit analyze github:owner/repo --record-findings
```

**JSON output schema:**

```json
{
  "portfolio_summary": {
    "avg_score": 74.5,
    "total_runs": 12,
    "unique_repos": 4,
    "top_issue": "missing-tools-section",
    "best_repo": "owner/repo-a",
    "worst_repo": "owner/repo-d"
  },
  "common_findings": [
    {"finding": "missing-tools-section", "repo_count": 3, "total_occurrences": 5}
  ],
  "outliers": [
    {"project": "owner/repo-d", "latest_score": 42.0, "avg_score": 48.5, "run_count": 2}
  ],
  "trending": [
    {"project": "owner/repo-b", "previous_score": 55.0, "latest_score": 80.0, "delta": 25.0, "direction": "up"}
  ]
}
```

## Publishing & Sharing

### Org Leaderboard (New in v0.58.0)

Publish a live org-wide AI-readiness leaderboard to GitHub Pages with one command:

```bash
# Score all public repos in an org and publish a leaderboard
agentkit pages-org github:myorg

# Publish from within agentkit org (after scoring)
agentkit org github:myorg --pages

# Options
agentkit pages-org github:myorg --pages-repo myorg/custom-scores
agentkit pages-org github:myorg --only-below 80   # only repos below score 80
agentkit pages-org github:myorg --limit 20
agentkit pages-org github:myorg --dry-run          # skip git push
agentkit pages-org github:myorg --quiet            # print URL only (cron mode)
```

The leaderboard is published to `https://<owner>.github.io/agentkit-scores/` by default.
Enable GitHub Pages on `<owner>/agentkit-scores` (Settings → Pages → branch: main, folder: /docs).

For weekly automated updates, use the example workflow:
`.github/workflows/examples/agentkit-org-pages.yml`

## Pages Trending: Daily AI-Ready Repo Leaderboard

`agentkit pages-trending` fetches today's trending GitHub repos, scores them for agent-readiness, and publishes a persistent dark-theme leaderboard to GitHub Pages at `https://<owner>.github.io/<repo>/trending.html`.

```bash
# Publish daily trending leaderboard (uses GITHUB_TOKEN)
agentkit pages-trending

# Filter to Python trending repos this week
agentkit pages-trending --language python --period week

# Dry run — score and generate HTML without pushing
agentkit pages-trending --dry-run

# Custom pages repo, limit 30 repos
agentkit pages-trending --pages-repo github:myorg/my-trending --limit 30

# Cron-friendly: print only the URL
agentkit pages-trending --quiet

# Publish + generate a 24h preview link
agentkit pages-trending --share
```

The leaderboard is published to `https://<owner>.github.io/<repo>/trending.html`.
For daily automated updates, use: `.github/workflows/examples/agentkit-trending-pages.yml`

## Sharing Results

Share your agent quality score card with a single command:

```bash
# Generate and upload a score card to here.now
agentkit share

# Share from a saved JSON report
agentkit share --report agentkit-report.json

# Hide raw numbers (show pass/fail only)
agentkit share --no-scores

# Output JSON with URL and score
agentkit share --json

# Auto-share after a run
agentkit run --share

# Auto-share after generating a report
agentkit report --share

# Quickest way to get a score + share URL for any repo
agentkit quickstart github:owner/repo

# Full analyze with share (more detail, slower)
agentkit analyze github:owner/repo --share

# Batch analyze repos and share a combined scorecard
agentkit sweep github:owner/repo1 github:owner/repo2 --share
```

Score cards are standalone HTML pages (dark theme) showing: composite score, per-tool breakdown, project name, git ref, and timestamp. Anonymous cards expire in 24h; set `HERENOW_API_KEY` for persistent links.

## Permanent Gist Links

```bash
# Publish a file as a permanent GitHub Gist
agentkit gist --from agentkit-report.md

# Publish a public gist (no token needed)
agentkit gist --from report.md --public

# Custom description
agentkit gist --from report.md --description "My project analysis"

# Auto-publish gist after run/report/analyze
agentkit run --gist
agentkit report --gist
agentkit analyze github:owner/repo --gist
```

Set `GITHUB_TOKEN` env var or run `gh auth login` for private gists. Public gists work without any token.

## GitHub Actions

Use the agentkit GitHub Action to run quality checks on every PR:

```yaml
- uses: mikiships/agentkit-cli@v0.7.0
  with:
    github-token: ${{ secrets.GITHUB_TOKEN }}
    min-score: 70
```

Or install and run directly:

```yaml
- uses: actions/checkout@v4
- run: pip install agentkit-cli
- run: agentkit gate --profile strict
```

See `agentkit setup-ci` for automated workflow generation.

## Local Dashboard

`agentkit serve` starts a lightweight local web dashboard showing all toolkit runs from the history database:

```
agentkit serve [OPTIONS]

Options:
  --port PORT    Port to serve on (default: 7890)
  --open         Auto-open the dashboard in your browser on start
  --once         Render dashboard HTML to stdout and exit (no server)
  --json         Print server URL as JSON and exit (useful for scripts)
```

The dashboard shows a dark-theme summary of every project run: latest score, grade (A–F), per-tool breakdown, timestamp, and run ID. Scores are color-coded green (≥80), yellow (≥60), and red (<60). The page auto-refreshes every 30 seconds.

**Quick start:**
```bash
agentkit serve --open           # start server + open browser
agentkit run --serve            # run pipeline, then print dashboard URL
agentkit serve --once > out.html  # render to file
```

No external dependencies — uses Python stdlib only (`http.server`, `threading`, `webbrowser`).

## Live Dashboard

Run once and watch scores update in real-time:

```bash
# Combined: watch files + serve dashboard (updates without reload)
agentkit watch --serve --port 7890

# Or start server in live mode (polls for external writes):
agentkit serve --live
```

The dashboard connects via SSE (`/events`) and re-renders the runs table in-place when new pipeline results arrive. A **● Live** indicator shows connection status; it drops to **○ Offline** if the server stops.

## agentkit pr — Submit CLAUDE.md PRs to Open Source Repos

`agentkit pr` is a viral distribution mechanic: one command generates a `CLAUDE.md` for any public GitHub repo and opens a PR against it.

```bash
# Submit a CLAUDE.md PR to a public repo
agentkit pr github:owner/repo

# Preview what would happen (no git or API calls)
agentkit pr github:owner/repo --dry-run

# Generate AGENTS.md instead
agentkit pr github:owner/repo --file AGENTS.md

# Force overwrite if CLAUDE.md already exists
agentkit pr github:owner/repo --force

# JSON output
agentkit pr github:owner/repo --json
```

**Requires:** `GITHUB_TOKEN` environment variable with `repo` and `workflow` scopes.

```bash
export GITHUB_TOKEN=ghp_...
agentkit pr github:vercel/next.js
```

What it does:
1. Clones the repo (shallow, depth 1)
2. Runs `agentmd generate .` to create CLAUDE.md
3. Forks the repo under your authenticated GitHub account (if needed)
4. Creates a branch `agentkit/add-claude-md`
5. Commits and pushes the generated file
6. Opens a PR against the original repo

## Release Check

`agentkit release-check` verifies the 4-part release surface to confirm a package is truly shipped, not just locally complete:

```
agentkit release-check [PATH] [OPTIONS]

Options:
  --version VERSION   Version to verify (default: from pyproject.toml/package.json)
  --package NAME      Package name (default: from pyproject.toml/package.json)
  --registry          pypi|npm|auto (default: auto-detected)
  --skip-tests        Skip the pytest/npm test step for quick checks
  --json              Output structured JSON for CI integration
```

Example output:

```
agentkit release-check — /your/project

┌────────────┬────────┬─────────────────────────────────┐
│ Check      │ Status │ Detail                          │
├────────────┼────────┼─────────────────────────────────┤
│ tests      │ ✓ PASS │ 42 passed in 1.23s              │
│ git_push   │ ✓ PASS │ Local HEAD abc12345 matches rem │
│ git_tag    │ ✓ PASS │ Tag v1.0.0 found on remote.     │
│ registry   │ ✓ PASS │ PyPI: mypkg==1.0.0 is live.    │
└────────────┴────────┴─────────────────────────────────┘

Verdict: SHIPPED
```

Verdict levels:
- **SHIPPED** — all 4 surfaces confirmed (exit 0)
- **RELEASE-READY** — tests + git confirmed, registry not yet live (exit 1)
- **BUILT** — tests pass locally, not yet pushed (exit 1)
- **UNKNOWN** — tests failing (exit 1)

Integrate with `agentkit gate --release-check` or `agentkit run --release-check` to add release verification to your pipeline.

## Architecture

All quartet tool invocations (agentmd, agentlint, coderace, agentreflect) go through `ToolAdapter` in `agentkit_cli/tools.py`. This ensures canonical correct flags are used everywhere and flag-wiring bugs cannot recur across subcommands.

Run `pytest -m smoke` before any release to catch integration regressions.

## Automated Improvement (`agentkit improve`)

`agentkit improve` closes the full loop: analyze → identify improvements → apply fixes → re-analyze → show delta.

```bash
agentkit improve
agentkit improve --dry-run
agentkit improve --no-generate --no-harden
agentkit improve --min-lift 10
agentkit improve --json
agentkit improve --output report.html
agentkit run --improve
```

## AI-Powered Explanations

`agentkit explain` calls an LLM (Claude via Anthropic API) to generate a human-readable coaching report explaining *why* your scores are what they are — not just what to fix, but what it means for agents working on your codebase.

```bash
# Explain scores for the current project (template mode, no API key needed)
agentkit explain --no-llm .

# Explain a saved run report (LLM mode, requires ANTHROPIC_API_KEY)
agentkit explain --report report.json

# Get structured JSON output
agentkit explain --no-llm . --json

# Save coaching report to a file
agentkit explain --no-llm . --output coaching.md

# Run the full pipeline then get a coaching report in one command
agentkit run --explain --no-llm .
```

The coaching report includes four sections:
- **What This Score Means** — plain language interpretation for your tier (A/B/C/F)
- **Key Findings Explained** — why each issue actually hurts agent performance
- **Top 3 Next Steps** — ordered by impact
- **If You Do Nothing Else** — the single most important action

Use `--no-llm` for offline mode (CI environments, no API key). Set `ANTHROPIC_API_KEY` for LLM-powered coaching via `claude-3-5-haiku-20241022`.

**The meta-angle:** "Your AI agent's AI quality score, explained by AI."

---

## agentkit certify

Generate a dated, shareable certification report proving a repo passed all agentkit quality checks.

```bash
# Run cert on current directory
agentkit certify .

# Output JSON cert (for CI integration)
agentkit certify . --json

# Write HTML cert card to file
agentkit certify . --output cert.html

# Share HTML report via here.now (requires HERENOW_API_KEY)
agentkit certify . --output cert.html --share

# Fail exit if composite score < 80
agentkit certify . --min-score 80

# Inject/update cert badge in README.md
agentkit certify . --badge

# Preview badge change without writing
agentkit certify . --badge --dry-run
```

The cert report includes:
- **cert_id**: 8-char hex fingerprint (prefix of SHA256)
- **timestamp**: UTC ISO 8601
- **verdict**: PASS / WARN / FAIL
- **Composite Score** (agentkit score) — PASS ≥ 80
- **Redteam Resistance** (agentkit redteam) — PASS ≥ 70
- **Context Freshness** (agentlint check-context) — PASS ≥ 70
- **Tests Found** (agentkit doctor)
- **SHA256** content hash for tamper detection

## License

MIT

## Timeline

`agentkit timeline` generates a dark-theme HTML chart showing your composite score progression over time. Reads from the existing SQLite history DB populated by `agentkit run`.

```bash
# Generate timeline for all projects
agentkit timeline

# Filter to one project
agentkit timeline --project my-agent

# Show only the last 20 runs since a date
agentkit timeline --limit 20 --since 2026-01-01

# Output raw chart data as JSON
agentkit timeline --json

# Publish and share
agentkit timeline --share

# Auto-generate timeline after a run
agentkit run --timeline
```

The report includes:
- **Main chart**: line chart (x = date, y = composite score), one line per project
- **Per-tool breakdown**: CSS-bar sparklines for lint score, code quality, context freshness, test count
- **Stats panel**: min/max/avg, trend direction (↑↓→), streak badge (e.g. "12 runs above 80")
- **Project summary table**: run count, latest score, trend per project

## Red-Team Your Agent Setup

`agentkit redteam` scores how well your agent context file (`CLAUDE.md` / `AGENTS.md`) resists adversarial attacks. Static analysis only — no LLM required. Truly model-agnostic.

```bash
# Analyze current directory
agentkit redteam

# Analyze a specific project
agentkit redteam ./my-agent-project

# CI gate: fail if resistance score < 70
agentkit redteam --min-score 70

# JSON output for programmatic use
agentkit redteam --json

# Save HTML report
agentkit redteam --output redteam-report.html

# Share HTML report via here.now
agentkit redteam --share
```

**Categories checked:**
- `prompt_injection` — attempts to inject instructions via user input
- `jailbreak` — persona and restriction bypass attempts
- `context_confusion` — fake context and history injection
- `instruction_override` — priority and mode override attempts
- `data_extraction` — system prompt and credential extraction
- `role_escalation` — privilege and authority escalation

**CI integration:**
```yaml
- name: Red-team agent config
  run: agentkit redteam --min-score 70
```

Exit code 1 if `--min-score` threshold is not met. Combine with `agentkit run --redteam` to add adversarial eval to your full pipeline.

**Distribution angle:** After OpenAI's $86M acquisition of Promptfoo, teams using non-OpenAI models need a neutral red-team tool. Static analysis = no model dependency = truly model-agnostic.

## Auto-Harden Your Agent Context

`agentkit harden` is the detect→fix loop closed in one command. Run it after `agentkit redteam` to auto-patch all detected vulnerabilities.

```bash
# Analyze and auto-remediate CLAUDE.md / AGENTS.md in cwd
agentkit harden

# Harden a specific file or directory
agentkit harden ./my-agent-project

# Preview what would change without writing
agentkit harden --dry-run

# Write hardened file to a different path
agentkit harden --output hardened-CLAUDE.md

# JSON output for CI integration
agentkit harden --json

# Generate dark-theme HTML score-card report
agentkit harden --report

# Apply fix flag in redteam command
agentkit redteam --fix

# Auto-apply with dry-run preview
agentkit redteam --fix --dry-run

# Run harden after full pipeline
agentkit run --harden
```

**What `agentkit harden` does:**
1. Detects all 6 vulnerability categories (prompt injection, jailbreak, context confusion, instruction override, data extraction, role escalation)
2. Applies targeted, idempotent remediations (never duplicates existing sections)
3. Creates a backup (`.bak`) before modifying files
4. Re-scores the hardened file and shows a before/after table

**Idempotent:** Running it multiple times on an already-hardened file makes no additional changes.

## `agentkit monitor` — Continuous Quality Monitoring

Set up continuous quality monitoring for your repos. Get notified on Slack or Discord when scores change significantly.

```bash
# Add a repo to monitor (default: daily, alert on 10-point change)
agentkit monitor add github:owner/repo

# Weekly schedule with Slack notification
agentkit monitor add github:owner/repo --schedule weekly --notify-slack https://hooks.slack.com/...

# Alert when score drops below 80 OR changes by 5+ points
agentkit monitor add github:owner/repo --min-score 80 --alert-threshold 5

# List all monitored targets (last score, next due, notify configured)
agentkit monitor list

# Force an immediate check on all due targets
agentkit monitor run

# Force-check a specific target
agentkit monitor run --target github:owner/repo

# Start the background daemon (polls every 60 seconds)
agentkit monitor start

# Check daemon status and next scheduled runs
agentkit monitor status

# View recent check history
agentkit monitor logs --limit 20

# Stop the daemon
agentkit monitor stop

# Remove a target
agentkit monitor remove github:owner/repo
```

**Schedules:** `hourly`, `daily` (default), `weekly`

**Notifications:** Configure Slack (`--notify-slack`), Discord (`--notify-discord`), or any generic webhook (`--notify-webhook`). Fires when `abs(score_delta) >= alert_threshold` (default 10 points) or score drops below `--min-score`.

**Daemon:** Runs as a background subprocess, writing structured JSON lines to `~/.agentkit/monitor.log`. PID stored in `~/.agentkit/monitor.pid`. Handles SIGTERM gracefully.

## GitHub Webhook Integration

`agentkit webhook` closes the "outside-in" CI loop: instead of only running agentkit from inside CI, GitHub pushes events to agentkit, which automatically analyzes the repo and fires notifications.

### Quick Start

```bash
# 1. Configure the HMAC secret (must match GitHub webhook settings)
agentkit webhook config --set-secret <YOUR_GITHUB_WEBHOOK_SECRET>

# 2. Start the server
agentkit webhook serve --port 8080

# Listening on http://localhost:8080
# Point your GitHub webhook at this URL (use ngrok for public exposure)
```

### Subcommands

| Command | Description |
|---|---|
| `agentkit webhook serve [--port P] [--secret S] [--no-verify-sig]` | Start the HTTP server |
| `agentkit webhook config [--show] [--set-secret S] [--set-port P] [--set-channel URL]` | Manage configuration |
| `agentkit webhook test [--event push|pull_request] [--repo REPO]` | Simulate an event locally |

### Configuration (`.agentkit.toml`)

```toml
[webhook]
port = 8080
secret = ""          # HMAC secret from GitHub webhook settings
notify_channels = [] # Reuse existing NotificationService channels
```

### How It Works

1. GitHub POSTs a `push` or `pull_request` event to your server.
2. Server verifies the `X-Hub-Signature-256` HMAC and responds 200 immediately.
3. Background thread calls `EventProcessor.process()`:
   - Runs `CompositeScoreEngine` on the repo.
   - Records the score in history DB (`agentkit history`).
   - Fires a notification if the score dropped by ≥ 5 points vs previous run.
   - Formats a PR comment body (logged to stdout; actual GitHub API posting is out of scope).

### Doctor Check

`agentkit doctor` reports webhook configuration health under the **Integrations** section:

```
integrations  webhook config  WARN  Webhook configured but HMAC secret is empty.
```

### Post Run Notification

```bash
agentkit run --webhook-notify
```

After the pipeline completes, POSTs a JSON summary to `notify.webhook_url` from `.agentkit.toml`.

## GitHub Checks API

When running in GitHub Actions, `agentkit run` and `agentkit gate` automatically post a native **GitHub Check Run** with your composite score, grade, and per-tool breakdown — visible directly in the PR UI.

### Automatic Mode (CI)

No extra config needed. When `GITHUB_ACTIONS=true` and `GITHUB_TOKEN` is available, check runs are posted automatically:

```yaml
# In your GitHub Actions workflow:
permissions:
  contents: read
  checks: write    # Required for Checks API

steps:
  - run: agentkit run --ci
    env:
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
```

Use `--no-checks` to disable, or `--checks` to force even when auto-detection fails.

### Manual Commands

```bash
agentkit checks verify              # test that Checks API is configured
agentkit checks post --score 87     # manually post a check run
agentkit checks status              # show last check run posted
```

### What Gets Posted

- **Title:** `Agent Quality: 87/100 (B)`
- **Summary:** composite score + gate verdict (PASS/FAIL)
- **Body:** markdown table of per-tool scores with pass/warn/fail indicators
- **Annotations:** one annotation per failing tool (score < 80)
- **Linked scorecard** if `--share` is active

## `agentkit topic`: Agent-Ready Repos by Topic

`agentkit topic <topic>` discovers the top GitHub repos for a topic and ranks them by agent-readiness score. Complements `user-rank` (contributors) with a repo-focused view.

```bash
# Rank top Python repos by agent-readiness
agentkit topic python

# Filter by language, limit results
agentkit topic llm --language python --limit 10

# Output as JSON
agentkit topic agents --json

# Save HTML report
agentkit topic rust --output rust-repos.html

# Publish shareable link
agentkit topic go --share
```

The output includes: ranked repo table with scores, grades, star counts, and descriptions; top-repo spotlight; grade distribution chart. Use `--topic-repos` flag in `agentkit run` to append topic ranking to any pipeline run.

## `agentkit topic-duel`: Head-to-Head Topic Comparison

`agentkit topic-duel <topic1> <topic2>` fetches the top repos for two GitHub topics and runs a head-to-head agent-readiness comparison. Answers "Which ecosystem is more AI-agent-ready?"

```bash
# Compare fastapi vs django
agentkit topic-duel fastapi django

# Score 8 repos per topic
agentkit topic-duel langchain llamaindex --repos-per-topic 8

# Output as JSON
agentkit topic-duel rust python --json

# Save HTML report
agentkit topic-duel pytorch tensorflow --output duel.html

# Publish shareable link
agentkit topic-duel fastapi flask --share
```

The output includes: side-by-side ranked tables for both topics, per-dimension comparison (avg score, top score, grade-A count, repo count), and a winner declaration. Use `--repos-per-topic` to control depth (1-10, default 5).

## `agentkit user-rank`: State of Agent Readiness by Topic

`agentkit user-rank <topic>` discovers the top GitHub contributors building in a topic/language and ranks them by agent-readiness score. Produces a "State of Agent Readiness in `<topic>`" report.

```bash
# Rank top Python contributors by agent-readiness
agentkit user-rank python

# Limit to top 10 and output JSON
agentkit user-rank rust --limit 10 --json

# Save HTML report to file
agentkit user-rank llm --output report.html

# Publish shareable link
agentkit user-rank go --share

# Quiet mode for CI/scripting
agentkit user-rank python --quiet
```

The output includes: ranked table with scores and grades, grade distribution, top-scorer spotlight, and mean score across all contributors. Share the dark-theme HTML report with `--share` to publish a `here.now` link.

## User Duel: Head-to-Head Developer Comparison

`agentkit user-duel` compares two GitHub developers' agent-readiness side-by-side. It runs `user-scorecard` for each and declares a winner per dimension.

```bash
# Basic comparison
agentkit user-duel github:tiangolo github:kennethreitz

# Limit repos per user and output JSON
agentkit user-duel github:mikiships github:tiangolo --limit 3 --json

# Share a duel report link
agentkit user-duel github:tiangolo github:kennethreitz --share

# Just print the winner (cron/scripting friendly)
agentkit user-duel github:tiangolo github:kennethreitz --quiet
```

Dimensions compared: avg_score, letter_grade, repo_count, agent_ready_repos. Overall winner is determined by majority of dimension wins. Tie-friendly output included.

## `agentkit user-tournament`

`agentkit user-tournament` runs a bracket-style agent-readiness tournament for N GitHub developers. Round-robin mode for ≤8 participants, bracket mode for >8. Champion is determined by wins with avg score as tiebreak.

```bash
# Run a tournament between three developers
agentkit user-tournament github:tiangolo github:kennethreitz github:mikiships

# Output as JSON
agentkit user-tournament github:tiangolo github:kennethreitz --json

# Publish and share HTML report
agentkit user-tournament github:tiangolo github:kennethreitz github:mikiships --share

# Save HTML to local file
agentkit user-tournament github:tiangolo github:kennethreitz --output tournament.html
```

Use `--limit N` to cap comparisons and `--quiet` for scripting-friendly champion-only output.

## `agentkit user-team`

`agentkit user-team` analyzes a GitHub org's top contributors for agent-readiness. Each contributor is scored via `UserScorecardEngine`, then results are aggregated into a team scorecard with a ranked table, aggregate grade, and grade distribution.

```bash
# Score the top 10 contributors of an org
agentkit user-team github:pallets

# Limit to 5 contributors
agentkit user-team github:django --limit 5

# Output as JSON
agentkit user-team github:pallets --json

# Save HTML report to file
agentkit user-team github:pallets --output team-report.html

# Publish and share HTML report
agentkit user-team github:pallets --share
```

Use `--quiet` for CI-friendly output (only prints share URL if --share is set).

## `agentkit user-rank`

`agentkit user-rank` discovers top GitHub contributors for a topic/language and ranks them by agent-readiness. Each contributor is scored via `UserScorecardEngine`, then ranked into a "State of Agent Readiness in `<topic>`" report with ranked table, mean score, grade distribution, and top-scorer spotlight.

```bash
# Rank top 20 Python contributors by agent-readiness
agentkit user-rank python

# Rank top 10 Rust contributors
agentkit user-rank rust --limit 10

# Output as JSON
agentkit user-rank python --json

# Save HTML report to file
agentkit user-rank python --output report.html

# Publish and share HTML report to here.now
agentkit user-rank python --share
```

Use `--quiet` for CI-friendly output (only prints share URL if --share is set).

Use `--topic <topic>` in `agentkit run` to include user-rank in the pipeline:
```bash
agentkit run --topic python
```

## `agentkit user-improve`

`agentkit user-improve` finds a GitHub user's lowest-scoring public repos and automatically improves them by generating CLAUDE.md context files and applying agent hardening. Displays a before/after quality lift report.

```bash
# Improve top lowest-scoring repos for a user
agentkit user-improve github:tiangolo

# Target repos scoring below 70, up to 10 repos
agentkit user-improve github:kennethreitz --below 70 --limit 10

# Dry run: show what would be improved without applying changes
agentkit user-improve github:mikiships --dry-run

# Output as JSON
agentkit user-improve github:tiangolo --json

# Publish HTML improvement report to here.now
agentkit user-improve github:tiangolo --share
```

Use `--limit N` (default 5, max 20) to control how many repos are targeted, and `--below N` (default 80) to set the quality threshold.

## `agentkit user-card`

`agentkit user-card` generates a compact, embeddable agent-readiness card for a GitHub user. The card shows grade, avg score, context coverage, agent-ready repo count, and top repo — all in a shareable dark-theme HTML card.

```bash
# Generate a card for a user
agentkit user-card github:tiangolo

# Publish card to here.now and get a shareable URL
agentkit user-card github:tiangolo --share

# Output as JSON
agentkit user-card github:tiangolo --json

# Quiet mode (cron-friendly): print only the URL
agentkit user-card github:tiangolo --share --quiet

# Include forks, analyze up to 20 repos
agentkit user-card github:mikiships --no-skip-forks --limit 20
```

Use `--limit N` (default 10, max 30) to control how many repos are analyzed. The HTML card includes a Markdown embed snippet as an HTML comment when `--share` is used.

## `agentkit user-badge`

`agentkit user-badge` generates a shields.io agent-readiness badge for a GitHub user's profile README — a viral mechanic for spreading agent-readiness awareness organically.

```bash
# Generate badge (runs full scorecard scan)
agentkit user-badge github:torvalds

# Fast mode — skip scan, generate from explicit score
agentkit user-badge github:torvalds --score 85

# Inject badge into local README.md (idempotent)
agentkit user-badge github:torvalds --score 85 --inject

# Preview inject without modifying files
agentkit user-badge github:torvalds --score 85 --inject --dry-run

# Write badge markdown to file
agentkit user-badge github:torvalds --score 85 --output badge.md

# JSON output
agentkit user-badge github:torvalds --score 85 --json

# Show badge after scorecard
agentkit user-scorecard github:torvalds --badge

# Show badge after user-card
agentkit user-card github:torvalds --badge
```

Badge grades: A≥90 (brightgreen), B≥75 (green), C≥60 (yellow), D≥45 (orange), F<45 (red).

Example badge: `[![Agent Readiness](https://img.shields.io/badge/agent--readiness-A%20%2892%2F100%29-brightgreen?style=flat-square)](https://pypi.org/project/agentkit-cli/)`

## `agentkit daily-duel` — Zero-Input Daily Repo Comparison

`agentkit daily-duel` automatically selects two contrasting GitHub repos, runs a head-to-head agent-readiness duel, and outputs tweet-ready text. No arguments required — the pair is deterministically chosen from 20+ presets based on today's date.

```bash
# Run today's daily duel (auto-selects pair)
agentkit daily-duel

# Use a custom seed for reproducibility
agentkit daily-duel --seed 2026-03-20

# Override the pair manually
agentkit daily-duel --pair tiangolo/fastapi pallets/flask

# Deep mode (includes redteam dimension)
agentkit daily-duel --deep

# Print JSON result
agentkit daily-duel --json

# Quiet mode — only print tweet text
agentkit daily-duel --quiet

# Upload HTML report and include URL in tweet
agentkit daily-duel --share

# Write HTML report to file
agentkit daily-duel --output report.html

# Show 7-day schedule preview (no analysis)
agentkit daily-duel --calendar
```

Output includes:
- Side-by-side duel table (scores, grades, dimension winners)
- Tweet-ready text (≤280 chars): `{repo1} vs {repo2} agent-readiness: ...`
- JSON written to `~/.local/share/agentkit/daily-duel-latest.json` (for cron/automation)

**Preset categories:** web-frameworks, http-clients, ml-ai, testing, async-networking, databases, js-frameworks, devtools (20+ pairs).

Example tweet output:
```
tiangolo/fastapi vs pallets/flask agent-readiness: tiangolo/fastapi 82/100 (B), pallets/flask 71/100 (B). Winner: tiangolo/fastapi on 3/4 dimensions.
```

## Leaderboard Page

Generate a public HTML leaderboard of top agent-ready GitHub repos by ecosystem:

```bash
# Generate leaderboard for all 5 ecosystems (python, typescript, rust, go, javascript)
agentkit leaderboard-page

# Specify ecosystems and limit
agentkit leaderboard-page --ecosystems python,typescript,rust --limit 10

# Write to docs/leaderboard.html for GitHub Pages
agentkit leaderboard-page --pages

# Output as JSON
agentkit leaderboard-page --json

# Generate embed badge for a repo
agentkit leaderboard-page --embed github:owner/repo --embed-only
```

The generated HTML features:
- Dark theme (#0d1117) with ecosystem tabs
- Ranked table per ecosystem with scores, grades, and star counts
- "Last updated" timestamp and "Powered by agentkit-cli" badge
- SEO: `<title>`, `<meta description>`, og: tags, JSON-LD ItemList schema

### Automated Weekly Updates

The `.github/workflows/update-leaderboard.yml` workflow runs every Monday, regenerates `docs/leaderboard.html`, and commits it back to the repository for GitHub Pages hosting.

---

## Static Site

`agentkit site` generates a multi-page static site from your scored repos — SEO-optimized, dark-themed, and ready for GitHub Pages.

```bash
# Generate site in ./site directory
agentkit site ./site

# With specific topics and share URL
agentkit site ./site --topics python,rust --share

# Copy to docs/ for GitHub Pages deployment
agentkit site ./site --deploy

# JSON summary output
agentkit site ./site --json --quiet
```

Generated pages:
- **`index.html`** — hero section, quick stats, topic grid, recent scores table
- **`topic/{name}.html`** — ranked repos per topic with score badges
- **`repo/{owner}/{repo}.html`** — full score breakdown, history chart, GitHub link
- **`sitemap.xml`** — all pages with lastmod dates for search engines

Options: `--topics`, `--limit`, `--base-url`, `--share`, `--deploy`, `--json`, `--quiet`.

Auto-update after runs: `agentkit run --site ./site` regenerates the index page.

## Populate & Deploy

Seed the history DB with real scored data, then publish to GitHub Pages in one pipeline:

```bash
# Fetch and score top repos for default topics (python, typescript, rust, go)
agentkit populate

# Custom topics and limit
agentkit populate --topics python,typescript --limit 20

# Dry run — see what would be scored without scoring
agentkit populate --dry-run

# One-shot: populate then generate and deploy site
agentkit populate && agentkit site --deploy

# All-in-one: fetch fresh data AND generate site in one command
agentkit site --live --topics python,rust --deploy
```

`agentkit populate` fetches top GitHub repos for each topic via the GitHub Topics API, scores each with `agentkit analyze`, and stores results in the history DB. The `--live` flag on `agentkit site` combines populate + generate into one command.

`agentkit site --deploy` copies the generated site into `docs/` (or `--deploy-dir`), commits, and pushes to GitHub Pages.
