This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Data-driven sprint retrospectives from git history — velocity, cycle/lead time, contributor insights, and churn hotspots. Use to run a retrospective, analyze team velocity or throughput, or generate a retro report from commit history.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
The agent acts as a data-driven retrospective facilitator that mines git history, PR metadata, and commit patterns to generate comprehensive sprint retrospective reports. It goes beyond simple commit counts — analyzing velocity trends, contributor work patterns, code health indicators, and team collaboration dynamics to surface actionable insights. Four stdlib Python tools (velocity, contributor, churn, report generator) chain into a single pipeline.
Keywords: sprint retrospective, velocity analytics, contributor insights, code churn, work sessions, cycle time, lead time, throughput, burndown, team health, collaboration metrics, bus factor, refactor ratio, hotspot analysis, conventional commits, session detection, deep work, improvement tracking
Before generating the retro report, confirm these inputs. If any is unknown or vague, ASK — do not assume:
--days or --since/--until (defines which commits count; the wrong window skews velocity and cycle-time)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
# Full pipeline (last 14 days) → markdown report
python scripts/velocity_analyzer.py --days 14 -f json > /tmp/v.json && \
python scripts/contributor_insights.py --days 14 -f json > /tmp/c.json && \
python scripts/code_churn_analyzer.py --days 14 -f json > /tmp/ch.json && \
python scripts/retro_report_generator.py -v /tmp/v.json -c /tmp/c.json -u /tmp/ch.json -s "Sprint 23"
All tools support --format text|json, --days N, --since/--until YYYY-MM-DD, and --repo /path.
Load the reference that matches the task — keep this file lean and pull detail on demand:
In Scope: Git history analysis for velocity, contributor, and code churn metrics; session detection via commit-timestamp gaps; commit-type classification via conventional-commit prefixes; markdown report generation with executive summary, dashboards, and action-item tracking; sprint-over-sprint comparison; bus factor and knowledge-silo identification.
Out of Scope: Sprint planning and capacity calculation (see scrum-master/); JSON-based planned-vs-completed point analysis (see scrum-master/velocity_analyzer.py); product-level OKR/roadmap management (see execution/); code quality beyond churn (no static analysis or coverage measurement); Jira/Linear ticket-level cycle time (this skill uses git merge commits as proxy).
Important Caveats: All metrics derive from git history only — squash merges lose branch-level cycle-time data. Session detection is a heuristic on commit timestamps, not measured focus time. Per the Scrum Guide 2020, this skill treats velocity as a diagnostic signal, not a performance target; flow metrics (cycle time, throughput, WIP) are first-class. Rotate facilitation formats every 3-5 sprints to prevent staleness.
| Integration | Direction | Description |
|---|---|---|
scrum-master/ | Complements | Git-based velocity supplements JSON-based sprint data; cross-reference for fuller picture |
senior-pm/ | Feeds into | Retro velocity trends inform executive reporting and portfolio health dashboards |
delivery-manager/ | Feeds into | Velocity trends help forecast sprint capacity and release timing |
agile-coach/ | Feeds into | Retro trend data identifies systemic patterns for coaching interventions |
execution/release-notes/ | Feeds into | Sprint commit data and type distribution inform release note generation |
| CI/CD Workflows | Automated | GitHub Actions runs the 4-tool pipeline on a cron schedule (see workflows reference) |
.retro-history/ | Bidirectional | Save sprint snapshots for trend tracking; load previous snapshots for comparison |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。