[Issue #42] Update /retro command to store learnings and create encoding issues #49

HugoNijhuis · 2026-01-07T19:27:45Z

HugoNijhuis commented

2026-01-07 19:27:45 +00:00

Closes #42

Summary

Updates /retro command to store learnings in the learnings/ folder and create encoding issues to update skills/commands/agents.

New Flow

Gather context → 2. Reflect → 3. Identify learnings → 4. Store learning file → 5. Create encoding issue → 6. Update learning with issue ref → 7. Connect to vision

Key Additions

Learning file template: Date, Context, Learning, Encoded In, Governance sections
Encoding issue template: References learning file, specifies target skill/command/agent
Cross-references: Learning file ↔ Issue for traceability
Encoding destinations table: Where different types of learnings should be encoded
Architecture vs Product repo guidance: Different workflows for each

Test Plan

/retro creates learning file in learnings/
/retro creates issue to encode learning into skill/command/agent
Learning file references the encoding issue
Encoding issue references the learning file
Flow handles both architecture repo and product repos

🤖 Generated with Claude Code

Closes #42 ## Summary Updates `/retro` command to store learnings in the `learnings/` folder and create encoding issues to update skills/commands/agents. ## New Flow 1. Gather context → 2. Reflect → 3. Identify learnings → 4. **Store learning file** → 5. **Create encoding issue** → 6. Update learning with issue ref → 7. Connect to vision ## Key Additions - **Learning file template**: Date, Context, Learning, Encoded In, Governance sections - **Encoding issue template**: References learning file, specifies target skill/command/agent - **Cross-references**: Learning file ↔ Issue for traceability - **Encoding destinations table**: Where different types of learnings should be encoded - **Architecture vs Product repo guidance**: Different workflows for each ## Test Plan - [x] /retro creates learning file in learnings/ - [x] /retro creates issue to encode learning into skill/command/agent - [x] Learning file references the encoding issue - [x] Encoding issue references the learning file - [x] Flow handles both architecture repo and product repos 🤖 Generated with [Claude Code](https://claude.com/claude-code)

HugoNijhuis added 1 commit 2026-01-07 19:27:46 +00:00

Update /retro command to store learnings and create encoding issues 8868eedc31

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>

HugoNijhuis commented

2026-01-07 19:28:42 +00:00

AI Code Review

This is an automated review generated by the code-reviewer agent.

Summary

Solid implementation of the learning capture flow. The cross-referencing structure is clear, and the architecture vs product repo guidance is well-defined. A few minor suggestions for completeness.

Findings

Code Quality

Well-structured flow: The 7-step process (gather → reflect → identify → store → create issue → update → vision) is logical and complete.
Good separation of concerns: Learning files serve as historical record, while encoding issues drive actionable changes.
Clear examples: The templates and bash examples make the process concrete.

Completeness of Learning Flow

Strong bidirectional references: Learning file → Issue (in "Encoded In") and Issue → Learning file (in description). This traceability is excellent.
Missing step details: Step 6 says "Update learning file" but doesn't show the bash command to edit it. Consider adding: Edit learnings/YYYY-MM-DD-short-title.md to replace "Pending: Issue #XX" with the actual issue number.
Label application: Step 5 shows issue creation but doesn't include --labels flag. The Labels section mentions labels but they're not in the example command.

Architecture vs Product Repo Guidance

Clear distinction: The guidance clearly separates where learning files go (architecture repo) vs where vision changes go (manifesto.md vs vision.md).
One ambiguity: In product repos, the instruction says "learning files should be added to the architecture repo" but doesn't specify how (switch repos? note it down? create PR?). Consider clarifying the workflow.

Cross-References

Excellent two-way linking: Issue references learning file path, learning file references issue number. This creates a strong audit trail.
README reinforcement: The learnings/README.md explains the "why" behind the flow, which helps future users understand the governance intent.

Style Notes

Consistent markdown formatting and code block usage throughout.
The encoding destinations table is duplicated (lines 90-98 and in learnings/README.md), which is intentional redundancy for discoverability.

Verdict

LGTM

The learning capture flow is complete and well-structured. Cross-references are clear and bidirectional. The architecture vs product repo guidance effectively addresses the multi-repo scenario. Minor suggestions above would improve clarity but aren't blocking.

## AI Code Review > This is an automated review generated by the code-reviewer agent. ### Summary Solid implementation of the learning capture flow. The cross-referencing structure is clear, and the architecture vs product repo guidance is well-defined. A few minor suggestions for completeness. ### Findings #### Code Quality - **Well-structured flow**: The 7-step process (gather → reflect → identify → store → create issue → update → vision) is logical and complete. - **Good separation of concerns**: Learning files serve as historical record, while encoding issues drive actionable changes. - **Clear examples**: The templates and bash examples make the process concrete. #### Completeness of Learning Flow - **Strong bidirectional references**: Learning file → Issue (in "Encoded In") and Issue → Learning file (in description). This traceability is excellent. - **Missing step details**: Step 6 says "Update learning file" but doesn't show the bash command to edit it. Consider adding: `Edit learnings/YYYY-MM-DD-short-title.md to replace "Pending: Issue #XX" with the actual issue number`. - **Label application**: Step 5 shows issue creation but doesn't include `--labels` flag. The Labels section mentions labels but they're not in the example command. #### Architecture vs Product Repo Guidance - **Clear distinction**: The guidance clearly separates where learning files go (architecture repo) vs where vision changes go (manifesto.md vs vision.md). - **One ambiguity**: In product repos, the instruction says "learning files should be added to the architecture repo" but doesn't specify *how* (switch repos? note it down? create PR?). Consider clarifying the workflow. #### Cross-References - **Excellent two-way linking**: Issue references learning file path, learning file references issue number. This creates a strong audit trail. - **README reinforcement**: The learnings/README.md explains the "why" behind the flow, which helps future users understand the governance intent. #### Style Notes - Consistent markdown formatting and code block usage throughout. - The encoding destinations table is duplicated (lines 90-98 and in learnings/README.md), which is intentional redundancy for discoverability. ### Verdict **LGTM** The learning capture flow is complete and well-structured. Cross-references are clear and bidirectional. The architecture vs product repo guidance effectively addresses the multi-repo scenario. Minor suggestions above would improve clarity but aren't blocking.

HugoNijhuis merged commit 8868eedc31 into main

2026-01-07 19:28:48 +00:00

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Reference: flowmade-one/architecture#49