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\n\n# delivery-intel\n\n**The repo trust engine for GitHub.**\n\n*Evidence-driven intelligence that tells you whether a repo is safe to adopt, healthy to change, and disciplined enough to trust in CI.*\n\n[![npm version](https://img.shields.io/npm/v/delivery-intel?color=cb3837&label=npm&logo=npm&logoColor=white)](https://www.npmjs.com/package/delivery-intel)\n[![npm downloads](https://img.shields.io/npm/dw/delivery-intel?color=cb3837&logo=npm&logoColor=white)](https://www.npmjs.com/package/delivery-intel)\n[![CI](https://img.shields.io/github/actions/workflow/status/ParthibanRajasekaran/delivery-intel/ci.yml?branch=main&label=CI&logo=githubactions&logoColor=white)](https://github.com/ParthibanRajasekaran/delivery-intel/actions/workflows/ci.yml)\n[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n[![TypeScript](https://img.shields.io/badge/TypeScript-5-3178c6?logo=typescript&logoColor=white)](https://www.typescriptlang.org)\n[![Node 18+](https://img.shields.io/badge/node-18%2B-339933?logo=nodedotjs&logoColor=white)](https://nodejs.org)\n\n
\n\n> delivery-intel answers three questions — with evidence, not guesses:\n> 1. **Should I trust this repo?** (trust score, 6 trust dimensions, 15 forensic signals)\n> 2. **Does it ship well and fail safely?** (DORA metrics, vulnerability scan, change fail rate)\n> 3. **How does it compare?** (side-by-side comparison of any two repos)\n>\n> Every metric shows its source, sample size, and confidence level. No fake precision.\n\n---\n\n## ⚡ Quick Start\n\n```bash\n# Trust verdict — should I adopt this repo? (trust score, dimensions, forensics)\nnpx delivery-intel facebook/react --mode adopt\n\n# Compare two repos side-by-side\nnpx delivery-intel --compare facebook/react vercel/next.js\n\n# Full delivery health check (v2 engine)\nnpx delivery-intel facebook/react --v2\n\n# Classic metrics dump (v1 engine)\nnpx delivery-intel facebook/react\n```\n\n```\n┌──────────────────────────────────────────────────────────────┐\n│ facebook/react │\n│ Grade A- Score 91/100 Confidence high │\n│ Trend ↑ +4 pts │\n└──────────────────────────────────────────────────────────────┘\n\n✓ NO POLICY VIOLATIONS\n──────────────────────────────────────────────────────────────\n ✓ All delivery health policies are passing.\n\nMETRICS (source · sample · confidence)\n──────────────────────────────────────────────────────────────\n ◆ Deploy Frequency 12.4/wk [high] deployment_statuses\n ◆ Change Lead Time 3.2h [high] commit_to_deploy (84 PRs)\n ● Recovery Time 0.8h [med] deployment_failures\n ◆ Change Fail Rate 4.8% [high] deployment_statuses\n ● Pipeline Failures 8.2% [high] workflow_runs (52 runs)\n\nSIGNAL QUALITY\n──────────────────────────────────────────────────────────────\n ◈ Production signal strength: high\n ✓ Scanned manifests: npm (package.json), Go (go.mod)\n\nSECURITY\n──────────────────────────────────────────────────────────────\n ✓ No known vulnerabilities found in scanned manifests\n```\n\n> Works with full URLs too: `npx delivery-intel https://github.com/vercel/next.js --v2`\n\n---\n\n## Why DORA metrics matter\n\nGoogle's [2024 DORA Report](https://cloud.google.com/blog/products/devops-sre/announcing-the-2024-dora-report) (32,000+ respondents, 10 years of data):\n\n| Metric | Elite teams | Low performers | Gap |\n|--------|------------|----------------|-----|\n| **Deploy Frequency** | On-demand (multiple/day) | < once per 6 months | **973×** |\n| **Lead Time for Changes** | < 1 hour | 1 – 6 months | **6,570×** |\n| **Change Failure Rate** | 0–15% | 46–60% | — |\n| **Time to Restore** | < 1 hour | 1 week – 1 month | **6,570×** |\n\nElite teams are **2× more likely to meet reliability targets** and **1.8× more likely to meet business goals** (DORA 2024).\n\n---\n\n Lead Time ★ Elite\n 3.2 hours median (0.1 days)\n\n Change Failure Rate ● High\n 4.8% (2 failed / 42 total runs)\n\n ◈ Vulnerability Scan (OSV.dev)\n ────────────────────────────────────────────────────────\n ✓ No known vulnerabilities found\n\n ◈ Suggestions\n ────────────────────────────────────────────────────────\n ✓ Looking good, no critical issues detected\n```\n\n> Works with full URLs too: `npx delivery-intel https://github.com/vercel/next.js`\n\n---\n\n## What It Measures\n\n| Metric | What it tells you | Source | Confidence |\n|--------|-------------------|--------|------------|\n| **Deploy Frequency** | How often code ships to production | Deployment statuses → deploys → Actions → PRs | Waterfall |\n| **Change Lead Time** | Commit → production (PR flow as fallback) | Deployments + commits | High / Low |\n| **Change Fail Rate** | % of deployments that failed or rolled back | Deployment statuses + heuristics | High / Low |\n| **Recovery Time** | Time from failure to successful recovery | Deployment failures + workflow runs | High / Low |\n| **Pipeline Failure Rate** | CI workflow run reliability (distinct from CFR) | Workflow Runs API | High |\n| **Deployment Rework Rate** | Rollback/revert/hotfix rate (inferred) | PR titles + labels + deployment refs | Low (flagged) |\n| **Vulnerabilities** | Known CVEs across 7 manifest ecosystems | [OSV.dev](https://osv.dev) batch API | — |\n| **Delivery Score** | Confidence-weighted composite (0–100) | All of the above | Aggregate |\n\n> Every metric exposes: data source · sample size · lookback window · confidence level.\n> Low-confidence signals contribute less to the composite score — no fake precision.\n\nSupported manifest ecosystems: **npm** · **pip** · **Go modules** · **Poetry** · **pnpm** · **Cargo** · **RubyGems**\n\n---\n\n## 🔍 Trust Engine\n\nThe trust engine (`--mode adopt`) computes a **Trust Score (0–100)** across 6 weighted dimensions, powered by 15 forensic signal detectors:\n\n| Dimension | Weight | Key signals |\n|-----------|--------|-------------|\n| **Maintenance freshness** | 1.0 | Days since last commit/release, commit cadence |\n| **CI reliability** | 0.9 | Pipeline failure rate, CI flakiness across workflows |\n| **Vulnerability exposure** | 0.9 | Critical/high CVE density from OSV.dev scan |\n| **Release hygiene** | 0.8 | Semver compliance, conventional commits, changelog |\n| **Contributor concentration** | 0.8 | Bus factor — single-maintainer risk |\n| **Change safety** | 0.7 | Rollback signals, rework density, review latency |\n\nTrust levels: **high** (≥75) · **moderate** (≥45) · **low** (<45) · **insufficient-evidence** (too few signals)\n\n### Compare mode\n\n```bash\nnpx delivery-intel --compare facebook/react vercel/next.js\nnpx delivery-intel --compare facebook/react vercel/next.js --json\n```\n\nRuns the trust engine on both repos and produces a structured comparison: per-dimension deltas, metric tier diffs, unique forensic signals, and a winner verdict.\n\n### Fix Packs with Trust Gain\n\nEvery fix pack now includes a **trust gain estimate** — how much each fix would improve the trust score:\n\n```jsonc\n{\n \"id\": \"fix-missing-dependabot\",\n \"title\": \"Enable Dependabot\",\n \"trustGain\": 15, // estimated trust score improvement\n \"rationale\": \"Automated dependency updates reduce vulnerability exposure...\"\n}\n```\n\n---\n\n## Usage\n\n### CLI (zero install)\n\n```bash\n# Verdict: grade, confidence, policy violations, evidence chain (v2 engine)\nnpx delivery-intel facebook/react --v2\n\n# Classic metrics output (v1 engine — backwards compatible)\nnpx delivery-intel facebook/react\n\n# Compare last 30 days vs prior 30 days\nnpx delivery-intel vercel/next.js --trend\n\n# Include workflow strain analysis\nnpx delivery-intel vercel/next.js --risk\n\n# AI-powered executive narrative (requires LLM key — falls back to template)\nnpx delivery-intel vercel/next.js --narrative\n\n# JSON output (v2 includes full evidence chain + policy result)\nnpx delivery-intel vercel/next.js --v2 --json\n\n# Save report to file\nnpx delivery-intel vercel/next.js --v2 --json --output report.json\n```\n\n### All flags\n\n| Flag | Description |\n|------|-------------|\n| `--v2` | Evidence-driven engine with grade, confidence, forensics, and policy |\n| `--mode ` | Analysis mode: `oss`, `adopt`, `pr`, `exec`, `platform` (implies `--v2`) |\n| `--pr-comment` | Write a PR guardrail comment to `delivery-intel-pr-comment.md` (use with `--v2`) |\n| `--fail-below N` | Exit code 2 if delivery score is below N |\n| `--block` | Enable blocking violations (use with `--v2 --fail-below`) |\n| `--compare` | Compare two repos side-by-side: `--compare repoA repoB` (implies `--v2`, defaults to adopt mode) |\n| `--json` | Output raw JSON instead of the formatted terminal report |\n| `--output ` | Write JSON to a file (can combine with `--json`) |\n| `--trend` | Show 30-day vs prior-30-day deltas for all metrics |\n| `--risk` | Include workflow strain analysis (velocity + stability signal) |\n| `--narrative` | Generate an executive summary (LLM or template fallback) |\n| `--token ` | GitHub token — prefer `gh auth login` instead |\n| `--no-spinner` | Disable the scanning animation (useful in CI logs) |\n| `--version` | Print version |\n| `--help` | Show help |\n\n### Web Dashboard\n\n```bash\ngit clone https://github.com/ParthibanRajasekaran/delivery-intel.git\ncd delivery-intel\nnpm install\nnpm run dev\n# → http://localhost:3000\n```\n\nPaste a repo URL and get an animated dashboard with score ring, DORA cards, charts, vulnerability table, and suggestions.\n\n### Docker\n\n```bash\n# Dashboard\ndocker compose up dashboard\n\n# CLI\nREPO=facebook/react docker compose run --rm cli\n```\n\n---\n\n## 📦 JSON Output Schema\n\nPass `--json` (or `--json --output report.json`) to get machine-readable output.\n\n```jsonc\n{\n \"repo\": { \"owner\": \"vercel\", \"repo\": \"next.js\" },\n \"fetchedAt\": \"2026-04-17T12:00:00.000Z\",\n \"overallScore\": 87, // 0–100\n \"doraMetrics\": {\n \"deploymentFrequency\": {\n \"deploymentsPerWeek\": 12.4,\n \"rating\": \"Elite\", // Elite | High | Medium | Low\n \"source\": \"merged_prs_fallback\" // deployments_api | merged_prs_fallback\n },\n \"leadTimeForChanges\": {\n \"medianHours\": 3.2,\n \"rating\": \"Elite\"\n },\n \"changeFailureRate\": {\n \"percentage\": 4.8,\n \"failedRuns\": 2,\n \"totalRuns\": 42,\n \"rating\": \"High\"\n }\n },\n \"vulnerabilities\": [\n {\n \"packageName\": \"lodash\",\n \"currentVersion\": \"4.17.15\",\n \"vulnId\": \"GHSA-xxxx-xxxx-xxxx\",\n \"summary\": \"Prototype pollution\",\n \"severity\": \"high\", // critical | high | medium | low\n \"aliases\": [\"CVE-2021-23337\"],\n \"fixedVersion\": \"4.17.21\"\n }\n ],\n \"suggestions\": [\n {\n \"category\": \"reliability\", // performance | reliability | security\n \"severity\": \"high\", // high | medium | low\n \"title\": \"High Pipeline Failure Rate\",\n \"description\": \"...\",\n \"actionItems\": [\"...\"]\n }\n ],\n \"dailyDeployments\": [0, 1, 2, 3, 1, 2, 3], // last 7 days, index 0 = 6 days ago\n // present only with --trend\n \"trend\": {\n \"windowDays\": 30,\n \"current\": { \"deploymentsPerWeek\": 12.4, \"leadTimeHours\": 3.2, \"changeFailureRate\": 4.8, \"score\": 87 },\n \"prior\": { \"deploymentsPerWeek\": 9.1, \"leadTimeHours\": 5.6, \"changeFailureRate\": 6.2, \"score\": 78 },\n \"deltas\": { \"deploymentsPerWeek\": 3.3, \"leadTimeHours\": -2.4, \"changeFailureRate\": -1.4, \"score\": 9 }\n },\n // present only with --risk\n \"riskScore\": {\n \"score\": 42,\n \"level\": \"moderate\", // low | moderate | high | critical\n \"cycleTimeDelta\": 0.12,\n \"failureRateDelta\": -0.05,\n \"sentimentMultiplier\": 1.0,\n \"summary\": \"...\"\n }\n}\n```\n\n---\n\n## 🔐 Authentication\n\n| Method | Setup | Best for |\n|--------|-------|----------|\n| **None** | Just run it | Public repos (60 req/hr) |\n| **`gh auth login`** | `brew install gh && gh auth login` | Daily use, token stays in OS keychain ✨ |\n| **`GITHUB_TOKEN`** | `export GITHUB_TOKEN=ghp_...` | CI environments |\n| **`--token`** | `--token ghp_...` | Quick one-off (avoid in CI) |\n\nToken resolution order: `--token` flag → `GITHUB_TOKEN` env → `gh auth token`\n\n> **Private repos** require a token with `repo` scope. For CI, use `${{ secrets.GITHUB_TOKEN }}`. It's auto-scoped and expires per job.\n\n---\n\n## CI Integration\n\n### GitHub Actions Marketplace action\n\nThe easiest way — use the action directly:\n\n```yaml\n# .github/workflows/delivery-intel.yml\nname: Delivery Health Check\n\non:\n push:\n branches: [main]\n pull_request:\n\njobs:\n analyze:\n runs-on: ubuntu-latest\n steps:\n - uses: ParthibanRajasekaran/delivery-intel@v1.6.0\n with:\n fail-below: '40' # fail the job if score drops below 40\n```\n\nOutputs available after the step: `score`, `deploy-frequency`, `lead-time`, `change-failure-rate`, `mean-time-to-restore`.\n\n### Evidence-backed PR guardrail (v2 engine)\n\nPosts a policy-aware comment on every PR — no block unless a real threshold is breached:\n\n```yaml\n steps:\n - name: Delivery Health Check\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: |\n npx delivery-intel@latest ${{ github.repository }} \\\n --v2 \\\n --pr-comment \\\n --fail-below 40 \\\n --no-spinner\n\n - name: Post PR Comment\n if: always() && github.event_name == 'pull_request'\n env:\n GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: |\n gh pr comment ${{ github.event.pull_request.number }} \\\n --body-file delivery-intel-pr-comment.md \\\n --repo ${{ github.repository }}\n```\n\nThe comment leads with a verdict (`✅ No block` / `⚠️ Warning` / `🚫 BLOCKED`), followed by a collapsible metrics table with source and confidence for every data point.\n\n### npx (custom pipeline)\n\n```yaml\n steps:\n - name: Run delivery-intel\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: npx delivery-intel@latest ${{ github.repository }} --v2 --json --output report.json\n\n - name: Check health score\n run: |\n SCORE=$(jq '.scores.delivery.score' report.json)\n echo \"Health score: $SCORE / 100\"\n if (( $(echo \"$SCORE < 40\" | bc -l) )); then\n echo \"::error::Score $SCORE is below threshold (40)\"\n exit 1\n fi\n```\n\n---\n\n## 🏅 Badge\n\nOnce you have the dashboard deployed, you can show a live delivery score in any README:\n\n```markdown\n[![Delivery Score](https://your-deployment-url/api/badge?repo=owner/repo)](https://github.com/ParthibanRajasekaran/delivery-intel)\n```\n\nThe `GET /api/badge?repo=owner/repo` endpoint returns a [Shields.io endpoint-badge](https://shields.io/badges/endpoint-badge) payload. Score maps to color: `< 20` red → `< 40` orange → `< 60` yellow → `< 80` green → `≥ 80` bright green. Results cached 5 minutes.\n\n> **Self-hosting**: deploy the dashboard (`npm run build && npm start` or `docker compose up dashboard`) and replace `your-deployment-url`.\n\n---\n\n## 🏗️ Architecture\n\n```\n┌──────────────────────────────────────────────────────────────┐\n│ delivery-intel │\n├──────────────┬───────────────┬───────────────────────────────┤\n│ CLI │ Dashboard │ CI Workflow / Action │\n│ (npx) │ (Next.js) │ (action.yml) │\n├──────────────┴───────────────┴───────────────────────────────┤\n│ Renderers (terminal, JSON, PR comment, compare output) │\n├──────────────────────────────────────────────────────────────┤\n│ Trust Engine · Compare Engine · Verdict Engine │\n│ (6 dimensions, 15 signals, trust score 0–100) │\n├──────────┬────────────┬──────────────┬───────────────────────┤\n│ Scoring │ Policies │ Fix Packs │ Recommendations │\n│ (conf- │ (gates, │ (trust gain │ (ranked actions) │\n│ weighted)│ blocking) │ estimates) │ │\n├──────────┴────────────┴──────────────┴───────────────────────┤\n│ DORA Metrics (6) · Confidence · Evidence Sources · Caveats │\n├──────────────────────────────────────────────────────────────┤\n│ Normaliser (raw API → typed EvidenceEvent stream) │\n├──────────┬────────────┬──────────────────────────────────────┤\n│ GitHub │ OSV.dev │ Extractors (7 ecosystems) │\n│ REST API │ Vuln API │ npm·pip·Go·Poetry·pnpm·Cargo·Gems │\n└──────────┴────────────┴──────────────────────────────────────┘\n```\n\n---\n\n## 🛠 Tech Stack\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
RuntimeTypeScript · Node.js 18+ · Next.js (App Router)
GitHub@octokit/rest · @octokit/graphql
VisualizationRecharts · Framer Motion · Tailwind CSS
SecurityOSV.dev (free, no auth)
Cachingioredis (optional, degrades gracefully)
QualityESLint · Prettier · Husky · Vitest · GitHub Actions CI
\n\n---\n\n## AIDT — Canary Trust Scoring\n\nAI tools accelerate delivery, but they introduce a **Velocity Paradox**: the same tooling that lets a developer ship more code per day also reduces the natural review friction that catches subtle regressions. A PR co-authored by Copilot or Claude may contain highly regular, syntactically correct code that passes linting and unit tests but encodes wrong assumptions — and because the code looks polished, reviewers spend less time on it. AIDT (AI-Integrated Delivery Telemetry) makes this risk visible by combining an AI authorship signal with a review density measurement and mapping both to a recommended canary deployment window.\n\n### CLI usage\n\n```bash\n# Standalone — analyse a single PR\nnpx delivery-intel owner/repo --aidt --pr 142\n\n# Combined with v2 engine — full delivery health + canary trust\nnpx delivery-intel owner/repo --v2 --aidt --pr 142\n\n# JSON output\nnpx delivery-intel owner/repo --aidt --pr 142 --format json\n```\n\nSet `COPILOT_ORG_TOKEN` in your environment to enable Heuristic 3 (Copilot acceptance rate). Without it the heuristic is skipped and its weight is redistributed proportionally across the other two signals.\n\n### Output shape\n\n| Field | Type | Description |\n|---|---|---|\n| `pr_number` | `number` | The PR that was analysed |\n| `ai_authorship_pct` | `number` (0–1) | Combined AI authorship probability |\n| `review_time_per_loc` | `number` | Seconds of review time per line changed |\n| `baseline_density` | `number` | Rolling 90-day median s/loc (or fallback) |\n| `risk_weight` | `number` (0–1, 3dp) | Composite risk weight |\n| `canary_duration_minutes` | `30 \\| 60 \\| 240` | Recommended canary window |\n| `risk_tier` | `\"low\" \\| \"medium\" \\| \"high\"` | Human-readable tier |\n| `authorship_signals.description_match` | `boolean` | H1: AI phrase found in PR title/body |\n| `authorship_signals.diff_entropy_score` | `number` | H2: diff regularity score (high = AI-like) |\n| `authorship_signals.copilot_acceptance_rate` | `number \\| null` | H3: org-level Copilot acceptance rate (null if unavailable) |\n| `baseline_source` | `\"computed\" \\| \"fallback\"` | Whether baseline was calculated or defaulted |\n| `recommendation` | `string` | Human-readable deployment guidance |\n\n### BASELINE\\_DENSITY\n\n`BASELINE_DENSITY` is the rolling 90-day median of `review_time_seconds / lines_changed` computed from merged PRs in the target repo that contain no AI authorship signals (description patterns, Copilot attribution, etc.). It answers the question: *how much review attention does a human-authored change in this repo typically receive per line?*\n\nIf fewer than 10 qualifying PRs exist in the 90-day window, the module falls back to **6.0 seconds/line** — a conservative estimate derived from typical open-source PR review patterns.\n\n### Calibration note\n\n> The thresholds used in AIDT (risk weight 0.70 and 0.45, Heuristic 3 weight 20%, review density baseline fallback 6.0 s/loc) are calibration starting points derived from a single production environment. You should recalibrate these thresholds against your own team's incident data before treating canary durations as hard gates.\n\n### Ethics note\n\n> **This module produces team-level trending signals. Do not use review density or authorship scores as individual engineer performance metrics.**\n\n---\n\n## 🤝 Contributing\n\nContributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for dev setup, coding standards, and workflow.\n\n```bash\ngit clone https://github.com/ParthibanRajasekaran/delivery-intel.git\ncd delivery-intel\nnpm install\nnpm run validate # lint + typecheck + test in one shot\n```\n\n---\n\n## 📄 License\n\n[MIT](LICENSE). Use it however you want.\n","readmeFilename":"README.md"}