Rai Story Review

Story Review

Purpose

Reflect on the completed story to extract learnings, persist patterns, reinforce behavioral signals, and emit calibration telemetry.

Mastery Levels (ShuHaRi)

• Shu: Follow all steps, answer all checkpoint questions with specific examples
• Ha: Adapt depth to story complexity, batch small story reviews
• Ri: Custom review patterns, integrate with team retrospectives

Context

When to use: After implementation is complete and tests pass. Before /rai-story-close.

Inputs: Completed story, progress log, passing test suite.

Steps

Step 1: Verify Tests Pass

Determine which test command to run using this priority chain:

1. Check .raise/manifest.yaml for project.test_command — if set, use it directly (configuration over convention)
2. Detect language from project.project_type in manifest, or scan file extensions of changed files (git diff --name-only)
3. Map language to default using the table below

# .raise/manifest.yaml — example
project:
  test_command: "cargo test --quiet"   # explicit override, highest priority
  project_type: rust

The table is a fallback — project.test_command always wins when present.

Step 2: Gather Data & Reflect

Review the story development: actual vs estimated time, blockers, plan deviations.

Heutagogical checkpoint — answer with specific examples:

1. What did you learn?
2. What would you change about the process?
3. Are there improvements for the framework?
4. What are you more capable of now?

Identify concrete improvements to skills, guardrails, or templates. Apply small improvements immediately; create issues for complex ones.

Step 3: Persist Patterns & Reinforce

Add new patterns worth preserving across sessions:

rai pattern add "Pattern description" -c "context,keywords" -t process --from S{N}.{M}

Types: process, technical, architecture, codebase.

Reinforce existing patterns — evaluate behavioral patterns loaded at session start:

rai pattern reinforce {pattern_id} --vote {1|0|-1} --from S{N}.{M}

Only evaluate patterns you consciously considered. 0 is correct for most patterns in any story.

Step 4: Document Retrospective

Create work/epics/e{N}-{name}/stories/s{N}.{M}-retrospective.md with:

• Summary (story ID, dates, estimated vs actual time)
• What went well / what to improve
• Heutagogical checkpoint answers
• Improvements applied
• Patterns added/reinforced

Step 5: Emit Calibration Telemetry

rai signal emit-calibration S{N}.{M} --size {XS|S|M|L} --estimated {minutes} --actual {minutes}

This feeds the velocity tracking system for future estimation accuracy.

Output

Quality Checklist

• Project language detected before running tests
• Tests pass with language-appropriate runner (gate)
• Heutagogical checkpoint answered with specific examples
• New patterns persisted via rai pattern add
• Behavioral patterns reinforced via rai pattern reinforce
• Calibration telemetry emitted
• Retrospective document created
• NEVER skip pattern reinforce — scoring system depends on it (RAISE-170)
• NEVER give vague checkpoint answers — be specific with concrete examples

References

• Previous: /rai-story-implement
• Next: /rai-story-close
• Pattern scoring: RAISE-170 (temporal decay + Wilson scorer)