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Author SHA1 Message Date
fac88cfcc7 Simplify /retro flow: issue first, encoding later
Changed the retro flow to:
1. Retro (any repo) → Issue (architecture repo)
2. Later: Encode issue into learning file + skill/command/agent

Key changes:
- Retro now only creates issues, not learning files
- Learning files are created when the issue is worked on
- All issues go to architecture repo regardless of source repo
- Added "When the Issue is Worked On" section for encoding guidance
- Clearer separation between capturing insights and encoding them

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-07 20:38:29 +01:00
8868eedc31 Update /retro command to store learnings and create encoding issues
Restructured retro flow to:
1. Store learnings in learnings/ folder (historical + governance)
2. Create encoding issues to update skills/commands/agents
3. Cross-reference between learning files and issues
4. Handle both architecture and product repos differently

Key changes:
- Learning file template with Date, Context, Learning, Encoded In, Governance
- Encoding issue template referencing the learning file
- Encoding destinations table (skill/command/agent/manifesto/vision)
- Clear guidance for architecture vs product repo workflows
- Updated labels (learning instead of retrospective)

Closes #42

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-07 20:27:29 +01:00

View File

@@ -1,13 +1,22 @@
---
description: Run a retrospective on completed work. Captures learnings, creates improvement issues, and updates product vision.
description: Run a retrospective on completed work. Captures insights as issues for later encoding into skills/commands/agents.
argument-hint: [task-description]
---
# Retrospective
Capture learnings from completed AI-assisted work to improve the workflow and refine the product vision.
Capture insights from completed work as issues on the architecture repo. Issues are later encoded into learnings and skills/commands/agents.
@~/.claude/skills/vision-management/SKILL.md
@~/.claude/skills/gitea/SKILL.md
## Flow
```
Retro (any repo) → Issue (architecture repo) → Encode: learning file + skill/command/agent
```
The retro creates the issue. Encoding happens when the issue is worked on.
## Process
@@ -18,75 +27,90 @@ Capture learnings from completed AI-assisted work to improve the workflow and re
- What worked well?
- Any specific improvement ideas?
3. **Analyze and categorize**: Group learnings into:
- **Prompt improvements**: Better instructions for commands/skills
- **Missing capabilities**: New commands or skills needed
- **Tool issues**: Problems with tea CLI, git, or other tools
- **Context gaps**: Missing documentation or skills
3. **Identify insights**: For each insight, determine:
- **What was learned**: The specific insight
- **Where to encode it**: Which skill, command, or agent should change?
- **Governance impact**: What does this mean for how we work?
4. **Connect to vision** (if `vision.md` exists in the target repo):
- Did this work make progress on any vision goals?
- Did learnings reveal new priorities that should become goals?
- Did we discover something that should be a non-goal?
- Should the current focus shift based on what we learned?
4. **Create issue on architecture repo**: Always create issues on `flowmade-one/ai`:
If any vision updates are needed:
- Present suggested changes to `vision.md`
- Ask for approval
- Update the vision file and sync to Gitea
```bash
tea issues create -r flowmade-one/ai \
--title "[Learning] <brief description>" \
--description "## Context
[Task that triggered this insight]
5. **Generate improvement issues**: For each actionable improvement:
- Determine the appropriate milestone (see Milestone Categorization below)
- Create an issue in the AI repo with the milestone assigned:
## Insight
[The specific learning - be concrete and actionable]
```bash
tea issues create -r flowmade-one/ai --title "<title>" --description "<body>" --milestone "<milestone>"
```
## Suggested Encoding
- [ ] \`skills/xxx/SKILL.md\` - [what to add/change]
- [ ] \`commands/xxx.md\` - [what to add/change]
- [ ] \`agents/xxx/agent.md\` - [what to add/change]
## Milestone Assignment
## Governance
[What this means for how we work going forward]"
```
Before creating issues, fetch available milestones:
5. **Connect to vision**: Check if insight affects vision:
- **Architecture repo**: Does this affect `manifesto.md`? (beliefs, principles, non-goals)
- **Product repo**: Does this affect `vision.md`? (product direction, goals)
```bash
tea milestones -f title,description
```
If vision updates are needed, present suggested changes and ask for approval.
For each issue, automatically assign to the most relevant milestone by matching:
- Issue content/problem area → Milestone title and description
- If no clear match, ask the user which milestone (goal) the issue supports
- If no milestones exist, skip milestone assignment
## When the Issue is Worked On
## Issue Format
When encoding a learning issue, the implementer should:
Use this structure for retrospective issues:
1. **Create learning file**: `learnings/YYYY-MM-DD-short-title.md`
```markdown
## Context
What task triggered this learning (brief).
```markdown
# [Learning Title]
## Problem / Observation
What was the friction point or insight.
**Date**: YYYY-MM-DD
**Context**: [Task that triggered this learning]
**Issue**: #XX
## Suggested Improvement
Concrete, actionable change to make.
## Learning
## Affected Files
- commands/xxx.md
- skills/xxx/SKILL.md
```
[The specific insight]
## Encoded In
- `skills/xxx/SKILL.md` - [what was added/changed]
- `commands/xxx.md` - [what was added/changed]
## Governance
[What this means for how we work]
```
2. **Update skill/command/agent** with the encoded knowledge
3. **Close the issue** with reference to the learning file and changes made
## Encoding Destinations
| Insight Type | Encode In |
|--------------|-----------|
| How to use a tool | `skills/[tool]/SKILL.md` |
| Workflow improvement | `commands/[command].md` |
| Subtask behavior | `agents/[agent]/agent.md` |
| Organization belief | `manifesto.md` |
| Product direction | `vision.md` (in product repo) |
## Labels
Add appropriate labels:
- `retrospective` - Always add this
Add appropriate labels to issues:
- `learning` - Always add this
- `prompt-improvement` - For command/skill text changes
- `new-feature` - For new commands/skills
- `new-feature` - For new commands/skills/agents
- `bug` - For things that are broken
## Guidelines
- Be specific and actionable - vague issues won't get fixed
- One issue per improvement (don't bundle unrelated things)
- Reference specific commands/skills when relevant
- Keep issues small and focused
- Skip creating issues for one-off edge cases that won't recur
- **Always create issues on architecture repo** - regardless of which repo the retro runs in
- **Be specific**: Vague insights can't be encoded
- **One issue per insight**: Don't bundle unrelated things
- **Encoding happens later**: Retro captures the issue, encoding is separate work
- **Skip one-offs**: Don't capture insights for edge cases that won't recur