Claude
572b8acf8c
Make the library multi-platform without duplicating content. Each skills/<name>/SKILL.md body remains the single source of truth; a new generator renders platform-ready exports from it. - scripts/build-exports.mjs — dependency-free Node generator with a PLATFORMS registry so new platforms (Gemini, Cursor, …) are a few lines. Ships ChatGPT exports at exports/chatgpt/<bundle>/<skill>/SYSTEM_PROMPT.md (172 skills), plus generated index READMEs. Supports --platform and --check. - exports/ — generated ChatGPT system prompts, ready to paste into a Custom GPT. - .github/workflows/check-generated.yml — fails a PR if exports or web/skills.json drift from the source skills. - README "Works With" now documents the ready-to-use exports and regen command. - CHANGELOG + SKILL-AUTHORING-STANDARD note the generated artifacts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
57 lines
2.7 KiB
Markdown
57 lines
2.7 KiB
Markdown
# Retrospective Analysis Skill
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Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened.
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## Required Inputs
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Ask the user for these if not provided:
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- **Sprint tickets: planned vs. completed**
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- **Carry-over tickets and reasons** (if known)
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- **Tickets reopened after closing** (quality signal)
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- **Any incidents or unplanned work** (scope creep signal)
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- **Sprint velocity vs. historical average** (trend context)
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## Process
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1. Calculate: completion rate, carry-over rate, unplanned work percentage
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2. Identify patterns: which ticket types were most likely to carry over? Which caused blockers?
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3. Note any process or communication breakdowns visible in the data
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4. Prepare 3 "Start / Stop / Continue" prompts based on the data — not generic, specific to this sprint
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5. Suggest 1 concrete experiment for the next sprint based on the biggest friction point
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6. **Validate** — Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable
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## Output Structure
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### Sprint [Number] Retrospective Brief
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**By the Numbers:**
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- Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%]
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- Unplanned work: [n] tickets ([%] of capacity)
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- Velocity: [points] vs. [average] average
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**What the Data Suggests:**
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[2-3 observations grounded in the numbers above]
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**Discussion Prompts:**
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- Start: [specific prompt based on this sprint's data]
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- Stop: [specific prompt based on this sprint's data]
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- Continue: [specific prompt based on this sprint's data]
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**Suggested Experiment for Next Sprint:**
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[One concrete, testable process change — with a specific success metric]
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## Quality Checks
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- [ ] Each Start/Stop/Continue prompt names a specific behaviour, not a vague category
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- [ ] The recommended experiment is testable in one sprint
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- [ ] Carry-over analysis identifies the ticket type or cause, not just the count
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- [ ] Data observations don't assign blame — they describe patterns
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- [ ] Velocity trend is mentioned in context (is this a one-off or a pattern?)
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## Anti-Patterns
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- [ ] Do not assign blame to individuals in the retrospective brief — observations must describe patterns, not people
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- [ ] Do not produce Start/Stop/Continue prompts that are vague categories — each must name a specific behaviour
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- [ ] Do not recommend an experiment that cannot be completed within one sprint — small, testable experiments only
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- [ ] Do not treat carry-over tickets as a velocity problem without first identifying the root cause category
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- [ ] Do not run the same retrospective format every sprint — vary the format to prevent engagement fatigue
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