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Retrospective

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Donchitos
retrospective

Sprint or milestone retrospective from completed work, velocity, blockers. Actionable insights for the next iteration.

Overview

PublisherDonchitos
RepositoryClaude-Code-Game-Studios
Skill nameretrospective
Stars
25.8K
Forks
3.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Donchitos on GitHub. Read the source before you install it.

Installation

Install the Retrospective AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/retrospective
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Donchitos/Claude-Code-Game-Studios.git /tmp/Claude-Code-Game-Studios
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Game-Studios/.claude/skills/retrospective .claude/skills/retrospective
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Retrospective in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Retrospective on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Retrospective is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

!bash "${CLAUDE_SKILL_DIR}/../../hooks/yaml-helper.sh" resolve_config --keys automation,workflow,qa.level

Every AskUserQuestion call follows .claude/docs/automation-modes.md (collaborative asks always · guided major-only · autonomous logs and proceeds; automation_always_ask categories always prompt).

Insufficient input — check this before producing any report

If the inputs this skill needs do not exist, the answer is "could not run" — not a filled-in report. Check first, and stop if the check fails.

  1. List the inputs this skill reads (data files, prior reports, profiler output, test results, registries, source code).
  2. For each, record FOUND or ABSENT — not "assumed present".
  3. If any input required for a section is ABSENT, that section is NOT ASSESSED — NO DATA. Do not estimate it, do not infer it from an adjacent artifact, and do not leave a mandated cell to be filled by whoever reads the template next.
  4. If every required input is ABSENT, stop and report NOT ASSESSED — NO DATA as the whole verdict, naming what was missing and which skill produces it.

A verdict of NOT ASSESSED is a success. It is the correct, useful answer to "what does the data say?" when there is no data. The failure mode this prevents is specific and has been observed in practice: report templates whose verdict enum had no "could not run" state produced false clean passes — an asset audit returning COMPLIANT on a project with no assets and no standards, and a performance profile reporting ">99% headroom against a 16.67ms budget" with zero profiler data and no budget ever set.

Absence of evidence is never evidence of absence. A scan that finds no matches because there are no files to scan has not verified anything. Say which of the two happened — a reader cannot tell from a green result.


Phase 1: Parse Arguments

Determine whether this is a sprint retrospective (sprint-N) or a milestone retrospective (milestone-name).


Phase 1b: Check for Existing Retrospective

Before loading any data, glob for an existing retrospective file:

  • For sprint retrospectives: production/retrospectives/retro-[sprint-slug]-*.md (also check production/sprints/sprint-[N]-retrospective.md as an alternate location)
  • For milestone retrospectives: production/retrospectives/retro-[milestone-name]-*.md

If a matching file is found, use AskUserQuestion:

  • Prompt: "An existing retrospective was found: [filename]. How do you want to proceed?"
  • Options:
    • [A] Update existing — load it and add/revise sections with new data
    • [B] Start fresh — generate a new retrospective (archive the old one)

If [A]: read the existing file and carry its content forward, revising sections with new data. If [B]: continue to Phase 2 with a blank slate. Before writing the new file, rename the existing one with a -archived-[date] suffix. That rename is a write too: it happens in Phase 5, only once the ask there — which names it — is approved.


Phase 2: Load Sprint or Milestone Data

Read the sprint or milestone plan from the appropriate location:

  • Sprint plans: production/sprints/
  • Milestone definitions: production/milestones/

Also check for production/sprint-status.yaml: if it exists, read it alongside the sprint plan. It is the authoritative source for actual story completion status (status: done, completed dates, blockers). Use it as the primary source for completion metrics in Phase 3. Fall back to markdown scanning only if the yaml does not exist. Note discrepancies between the yaml and the sprint plan (e.g., stories in yaml not in plan, or vice versa).

If the file does not exist or is empty, output:

"No sprint data found for [sprint/milestone]. Run /sprint-plan new to create a sprint (/sprint-status only reads one), or provide the sprint details manually."

At workflow: minimal (resolved above) there are no sprints: offer to look back over the stories closed so far instead (production/epics/*/story-*.md with Status: Complete). At qa.level: minimal (resolved above) tests are waived, so a story closed without one is not a finding; a Visual/Feel or UI story closed without its retained screenshot still is.

Then use AskUserQuestion to present these options ([C] only at workflow: minimal):

  • [A] Provide data manually — ask the user to paste or describe the sprint tasks, dates, and outcomes; use that as the source of truth for the retrospective.
  • [B] Stop — abort the skill. Verdict: BLOCKED — no sprint data available.
  • [C] Look back over the closed stories — use the stories with Status: Complete as the record of what was done.

If the user chooses [A], collect the data and continue to Phase 3 using what they provide. If the user chooses [B], stop here. If the user chooses [C], read those stories and continue to Phase 3. Sections that need sprint data (planned vs actual, velocity, carryover, estimation accuracy) are NOT ASSESSED — NO DATA.

Extract: planned tasks, estimated effort, owners, and goals.

Run git log for the sprint period to understand what was actually committed and when. Use the Bash tool (which uses Git Bash on Windows — the 2>/dev/null is bash syntax, not PowerShell):

Bash: git log --oneline --since="4 weeks ago" 2>/dev/null || git log --oneline -20

Adjust the --since date to match the sprint duration if known from the sprint plan.


Phase 3: Analyze Completion and Trends

Scan for completed and incomplete tasks by comparing the plan against actual deliverables. Check for:

  • Tasks completed as planned
  • Tasks completed but modified from the plan
  • Tasks carried over (not completed)
  • Tasks added mid-sprint (unplanned work)
  • Tasks removed or descoped

Every story with status: blocked in production/sprint-status.yaml goes under Blockers Encountered, with its blocker field as the Blocker, and gets an action item in Action Items for Next Iteration.

Scan the codebase for TODO/FIXME trends:

  • Count current TODO/FIXME/HACK comments
  • Compare to previous sprint counts if available (check previous retrospectives)
  • Note whether technical debt is growing or shrinking

Read previous retrospectives (if any) from production/retrospectives/ to check:

  • Were previous action items addressed?
  • Are the same problems recurring?
  • How has velocity trended?

With no earlier sprint or retrospective, Velocity Trend keeps only the current sprint's row and reads NOT ASSESSED — NO DATA, and Previous Action Items Follow-Up is that one line — no invented earlier rows, no empty table.


Phase 4: Generate the Retrospective

markdown
## Retrospective: [Sprint N / Milestone Name]
Period: [Start Date] -- [End Date]
Generated: [Date]

### Metrics

| Metric | Planned | Actual | Delta |
|--------|---------|--------|-------|
| Tasks | [X] | [Y] | [+/- Z] |
| Completion Rate | -- | [Z%] | -- |
| Story Points / Effort Days | [X] | [Y] | [+/- Z] |
| Bugs Found | -- | [N] | -- |
| Bugs Fixed | -- | [N] | -- |
| Unplanned Tasks Added | -- | [N] | -- |
| Commits | -- | [N] | -- |

### Velocity Trend

| Sprint | Planned | Completed | Rate |
|--------|---------|-----------|------|
| [N-2] | [X] | [Y] | [Z%] |
| [N-1] | [X] | [Y] | [Z%] |
| [N] (current) | [X] | [Y] | [Z%] |

**Trend**: [Increasing / Stable / Decreasing — or `NOT ASSESSED — NO DATA` with no earlier sprint to compare]
[One sentence explaining the trend]

### What Went Well
- [Observation backed by specific data or examples]
- [Another positive observation]
- [Recognize specific contributions or decisions that paid off]

### What Went Poorly
- [Specific issue with measurable impact -- e.g., "Feature X took 5 days
  instead of estimated 2, blocking tasks Y and Z"]
- [Another issue with impact]
- [Do not assign blame -- focus on systemic causes]

### Blockers Encountered

| Blocker | Duration | Resolution | Prevention |
|---------|----------|------------|------------|
| [What blocked progress] | [How long] | [How it was resolved] | [How to prevent recurrence] |

### Estimation Accuracy

| Task | Estimated | Actual | Variance | Likely Cause |
|------|-----------|--------|----------|--------------|
| [Most overestimated task] | [X] | [Y] | [+Z] | [Why] |
| [Most underestimated task] | [X] | [Y] | [-Z] | [Why] |

**Overall estimation accuracy**: [X%] of tasks within +/- 20% of estimate

[Analysis: Are we consistently over- or under-estimating? For which types of
tasks? What adjustment should we apply?]

### Carryover Analysis

| Task | Original Sprint | Times Carried | Reason | Action |
|------|----------------|---------------|--------|--------|
| [Task that was not completed] | [Sprint N-X] | [N] | [Why] | [Complete / Descope / Redesign] |

### Technical Debt Status
- Current TODO count: [N] (previous: [N])
- Current FIXME count: [N] (previous: [N])
- Current HACK count: [N] (previous: [N])
- Trend: [Growing / Stable / Shrinking]
- [Note any areas of concern]

### Previous Action Items Follow-Up

| Action Item (from Sprint N-1) | Status | Notes |
|-------------------------------|--------|-------|
| [Previous action] | [Done / In Progress / Not Started] | [Context] |

### Action Items for Next Iteration

| # | Action | Owner | Priority | Deadline |
|---|--------|-------|----------|----------|
| 1 | [Specific, measurable action] | [Who] | [High/Med/Low] | [When] |
| 2 | [Another action] | [Who] | [Priority] | [When] |

### Process Improvements
- [Specific change to how we work, with expected benefit]
- [Another improvement -- keep it to 2-3 actionable items, not a wish list]

### Summary
[2-3 sentence overall assessment: Was this a good sprint/milestone? What is
the single most important thing to change going forward?]

Phase 5: Save Retrospective

Present the retrospective and top findings to the user (completion rate, velocity trend, top blocker, most important action item).

Ask: "May I write this to production/retrospectives/retro-sprint-[N]-[date].md?" (or production/retrospectives/retro-[milestone-name]-[date].md for milestone retrospectives)

After Start fresh in Phase 1b, the same ask also names the rename: "…and rename [old file] to [old file name]-archived-[date].md?" On yes, rename first, then write; on no, neither happens.

If yes, write the file, creating the production/retrospectives/ directory if needed. Verdict: COMPLETE — retrospective saved.

If no, stop here. Verdict: BLOCKED — user declined write.


Phase 6: Next Steps

Use AskUserQuestion:

  • Prompt: "Retrospective complete. The action items and velocity data are ready. Would you like to start sprint planning now with this data pre-loaded?"
  • Options:
    • [A] Yes — open sprint planning with retro action items and velocity delta pre-populated
    • [B] No — I'll reference the retrospective file manually when I'm ready

If the user selects [A]: Proceed to invoke /sprint-plan new, passing the retrospective file path and a summary of the action items and velocity change so the sprint planner can reference them.

  • If this was a milestone retrospective, run /gate-check to formally assess readiness for the next phase.

Guidelines

  • Be honest and specific. Vague retrospectives ("communication could be better") produce vague improvements. Use data and examples.
  • Focus on systemic issues, not individual blame.
  • Limit action items to 3-5. More than that dilutes focus.
  • Every action item must have an owner and a deadline.
  • Check whether previous action items were completed. Recurring unaddressed items are a process smell.
  • If this is a milestone retrospective, also evaluate whether the milestone goals were achieved and what that means for the overall project timeline.

Frequently asked questions

What does the Retrospective AI skill do?

Sprint or milestone retrospective from completed work, velocity, blockers. Actionable insights for the next iteration.

Why use Retrospective on TypingMind?

Because you install it once and use it with any model. Retrospective is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Retrospective in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/retrospective. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Retrospective?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Retrospective?

As many as you like. As long as a model supports skills, you can use Retrospective with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Retrospective AI skill free?

Yes. It is published on GitHub by Donchitos under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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