68 plug-and-play, best-practice product management skills for AI agents: 30 Triple Diamond phase + 11 foundation + 12 utility + 15 tool (Foundation Sprint + Design Sprint). Plus 6 sub-agents, workflows, 200+ output samples, guides, and CI-enforced contracts. Apache 2.0.
C-Rank Grade: A (Welcoming) - 57/100
External PR Merge Rate: 86%
Response Time: 8h
First Timer Success: 100%
product-on-purpose/pm-skills has a recorded first-timer success rate of 100.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in product-on-purpose/pm-skills respond to incoming external pull requests in approximately 7.5 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 57.0 places product-on-purpose/pm-skills in the A tier.
The external contributor pull request merge rate for product-on-purpose/pm-skills is 85.5%, based on public PR activity from non-core contributors.
product-on-purpose/pm-skills currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
68 plug-and-play, best-practice product management skills for AI agents: 30 Triple Diamond phase + 11 foundation + 12 utility + 15 tool (Foundation Sprint + Design Sprint). Plus 6 sub-agents, workflows, 200+ output samples, guides, and CI-enforced contracts. Apache 2.0.
Strict review standards in product-on-purpose/pm-skills. Ensure PR scope matches issue requirements closely before requesting review.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 16:00 UTC (16:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 16:00 to 20:00 UTC (16:00 - 20:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~5 hours (96% approval). Large PRs (>500 lines) take ~36 hours (38% approval). Keep your diff compact for rapid review.
Successor to #280, which is scoped to the first of the four conditional sections added in v2.33.0. PR #281 takes develop-adr -> Model Choice, so three remain unclaimed. Pick one: Skill New conditional section Template deliver-prd (2.3.0) AI Behavior and Evaluation skills/deliver-prd/references/TEMPLATE.md deliver-prd (2.3.0) Agent Execution Contract skills/deliver-prd/references/TEMPLATE.md measure-instrumentation-spec (2.3.0) Model Trace Capture skills/measure-instrumentation-spec/references/TEMPLATE.md How The sections are conditional: they appear only when the scenario warrants them, so your sample's scenario must involve an AI- or model-backed feature. Pick one of the three fictional company threads (storevine, brainshelf, workbench) and stay in its voice per library/skill-output-samples/THREAD_PROFILES.md. Follow library/skill-output-samples/SAMPLE_CREATION.md: mirror the skill's references/TEMPLATE.md section order and mark every invented metric [fictional]. Adding
v2.33.0 added four conditional sections across three skills, and none of the ~212 samples in library/skill-output-samples/ exercises any of them yet (verified 2026-08-18: zero matches for all four headings across the library). Pick one and author the first sample that makes it fire: Skill New conditional section Template deliver-prd (2.3.0) AI Behavior and Evaluation skills/deliver-prd/references/TEMPLATE.md deliver-prd (2.3.0) Agent Execution Contract skills/deliver-prd/references/TEMPLATE.md measure-instrumentation-spec (2.3.0) Model Trace Capture skills/measure-instrumentation-spec/references/TEMPLATE.md develop-adr (2.2.0) Model Choice (inside Consequences) skills/develop-adr/references/TEMPLATE.md How The sections are conditional: they appear only when the scenario warrants them, so your sample's scenario must involve an AI- or model-backed feature. Pick one of the three fictional company threads (storevine, brainshelf, workbench) and stay in its voice per library
The getting-started quickstart walks a new user from install to first artifact in text only. A single annotated screenshot (or a short gif) of invoking one skill in Claude Code and receiving the artifact would make the verify-it-worked step concrete. Scope: one image asset under the docs site, embedded in site/src/content/docs/getting-started/quickstart.md near the verify step. Keep it client-neutral or label the client shown. Acceptance: the site builds clean (npm --prefix site run build) and check-rendered-links stays green.
The sample library at library/skill-output-samples/ carries ~210 samples across three fictional company threads (storevine, brainshelf, workbench). Pick a skill with fewer than 3 samples (one per thread is the coverage floor; scripts/check-skill-sample-coverage reports gaps) and author one sample following library/skill-output-samples/SAMPLE_CREATION.md and THREAD_PROFILES.md: mirror the skill's references/TEMPLATE.md section order, mark every invented metric [fictional], and match the thread voice. Acceptance: the sample invariant checks pass (check-sample-no-placeholders, check-sample-no-fabricated-metrics) and check-skill-sample-coverage improves for that skill.
Pick any skill without an evals/trigger-fixtures.json (after v2.30.0 lands, the registered roster at scripts/trigger-eval-roster.yaml makes the gap list visible: 31 of 68 skills are covered today) and author its fixture set per the existing contract: at least 16 queries, a 60/40 should-trigger split, and at least 2 near-miss negatives aimed at a declared collision partner. scripts/check-trigger-fixtures.mjs validates the shape deterministically, so you can iterate locally without any API key. Acceptance: node scripts/check-trigger-fixtures.mjs passes with your skill registered in the roster; the near-miss negatives name a real sibling skill.
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When evaluating whether to contribute to product-on-purpose/pm-skills, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of product-on-purpose/pm-skills acknowledge new external contributions in approximately 7 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 85.5% were successfully merged into the primary branch.
product-on-purpose/pm-skills has a recorded first-timer success rate of 100.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~7 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 57.0 places product-on-purpose/pm-skills in the Welcoming tier.
The external pull request merge rate is 85.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, product-on-purpose/pm-skills currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for product-on-purpose/pm-skills are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.