---

name: retro version: 2.3.0 description: "Skill & agent-centric retrospective with auto-collection, entity extraction, and skill suggestions. Invoke when: user calls /retro, finishes significant work, or wants to reflect on tooling effectiveness. Captures skill gaps, friction, agent evaluation, extracts entities, and creates improvement issues." allowed-tools: [Read, Bash, Glob, Grep, Write, AskUserQuestion] lifecycle: status: active created_at: "2026-01-21" updated_at: "2026-01-26" history:

• version: 2.3.0 date: "2026-01-26" changes: "Added auto skill suggestions based on friction patterns, repeated agents, and coverage gaps."
• version: 2.2.0 date: "2026-01-26" changes: "Added entity extraction to Obsidian vault. Extracted entities written to obsidian-vault/entities/"
• version: 2.1.0 date: "2026-01-26" changes: "Simplified: removed auto-trigger ceremony, made opt-in not ritual. Removed references to deleted skills."
• version: 2.0.0 date: "2026-01-26" changes: "Major rewrite: skill-centric + agent-centric evaluation, auto-collection, beads issue creation" manual_validated: "2026-01-26 - scenario: '/retro --quick' - PASS (6/6: auto-collection, structured report, skill+agent eval, AX state, CLEAR check, no errors)"
• version: 1.0.0 date: "2026-01-21" changes: "MVP: list closed, reflect on specific, adaptive prompts, dual storage"

---

/retro — Skill & Agent Centric Retrospective

TL;DR: Auto-collect session data (skills, agents, friction), analyze tooling effectiveness, and create actionable improvement issues. The "R" in CLEAR.

---

Quick Reference

/retro                  # Full retro with auto-collection
/retro <issue-id>       # Retro focused on specific work
/retro --quick          # Minimal prompts, max automation
/retro --verbose        # Include all collected data in output

---

Core Philosophy

Retro is about tooling, not feelings. (model)

Traditional retros ask "what went well/badly?" — subjective and hard to act on. This skill focuses on tooling effectiveness:

• Which skills helped? Which caused friction?
• Which agents worked? What's missing?
• What can we automate for next time?

Key insight: Every retro feeds /skill-farm — the system improves itself.

---

What Gets Auto-Collected

/retro automatically analyzes the session for:

Data Type	Source	Purpose
Skills invoked	<command-name> tags, state blocks	Skill usage patterns
Skill outcomes	State blocks, completion indicators	Success/failure analysis
Agents spawned	Task tool calls	Agent effectiveness
Friction events	Tool errors, retries, workarounds	Pain point identification
Entities	Skills, agents, tools, patterns	Knowledge graph population

No manual logging required — the skill reconstructs this from conversation context.

---

Entity Extraction

Entities are extracted and persisted to obsidian-vault/entities/ for knowledge graph building.

Entity Types

Type	Source	Example
skill	/skill-name invocations	/meta, /retro
agent	Task tool calls	Explore, general-purpose
tool	Tool invocations	Read, Write, Bash
pattern	Repeated behaviors	"parallel agents", "CLEAR check"
concept	Key terms mentioned	"L-labels", "AX", "state blocks"

Auto-Extraction Rules

EXTRACT_ENTITIES():
    entities = []

    # Skills → entity files
    FOR EACH skill IN collected_skills:
        entities.push({
            type: "skill",
            name: skill.skill_name,
            context: skill.outcome,
            frequency: COUNT occurrences
        })

    # Agents → entity files
    FOR EACH agent IN collected_agents:
        entities.push({
            type: "agent",
            name: agent.subagent_type,
            context: agent.description,
            frequency: COUNT occurrences
        })

    # Patterns → extract from repeated behaviors
    IF detected_patterns:
        FOR EACH pattern IN detected_patterns:
            entities.push({
                type: "pattern",
                name: pattern.name,
                context: pattern.description
            })

    RETURN entities

Entity Persistence

PERSIST_ENTITIES(entities):
    vault_path = "obsidian-vault/entities"

    FOR EACH entity IN entities:
        filename = SLUGIFY(entity.name) + ".md"
        filepath = vault_path + "/" + filename

        IF EXISTS(filepath):
            # Update existing entity
            content = Read(filepath)
            UPDATE frequency in frontmatter
            ADD new occurrence to table
            Write(filepath, updated_content)
        ELSE:
            # Create new entity from template
            content = GENERATE from templates/entity.md
            FILL IN:
                - entity_type: entity.type
                - name: entity.name
                - description: entity.context
                - frequency: 1
                - last_seen: TODAY
            Write(filepath, content)

---

Data Collection Schema

session_data:
  skills:
    - skill_name: string        # e.g., "clarify", "action"
      invoked_at: timestamp
      arguments: string | null
      outcome: "success" | "partial" | "failed" | "interrupted"
      friction_signals:
        - type: "retry" | "workaround" | "abandoned"
          description: string

  agents:
    - subagent_type: string     # e.g., "general-purpose", "Explore"
      description: string
      spawned_at: timestamp
      outcome: "success" | "failed" | "timeout"
      parent_skill: string | null

  friction:
    - tool: string
      error_type: string
      recovery: "retry" | "workaround" | "abandoned" | "asked_user"
      context: string

---

Evaluation Frameworks

Skill Evaluation

Analysis	Question	Output
Gaps	What skills were needed but don't exist?	New skill proposals
Friction	Where did existing skills slow things down?	Improvement suggestions
Coverage	What should existing skills do better?	Missing capabilities

Agent Evaluation

Analysis	Question	Output
Skill Needs	Which skills should agents have access to?	Agent tool additions
Missing Agents	What specialized agents would help?	New agent proposals
Orchestration	How can agent coordination improve?	Pattern improvements

---

Output Format

UX Layer (Human-Readable)

## Retro: [Session/Feature Name]

**Session Summary**: X skills | Y agents | Z friction events

---

### 🎯 What Worked
- [Auto-generated + user additions]

### ⚠️ What Didn't Work
- [Auto-generated + user additions]

### 🔄 Patterns Discovered
- [Extracted patterns]

---

### 🛠️ Skill Evaluation

#### Gaps (Missing Skills)
| Gap | Evidence | Suggested Skill | Priority |
|-----|----------|-----------------|----------|

#### Friction (Existing Skills)
| Skill | Issue | Suggested Fix |
|-------|-------|---------------|

#### Coverage (Skill Improvements)
| Skill | Missing Capability | Use Case |
|-------|-------------------|----------|

---

### 🤖 Agent Evaluation

#### Skill Needs
| Agent Type | Needed Skill | Rationale |
|------------|--------------|-----------|

#### Missing Agents
| Proposed Agent | Use Case | Skills Needed |
|----------------|----------|---------------|

#### Orchestration
| Current Pattern | Improvement |
|-----------------|-------------|

---

### 🔍 Entities Extracted

| Entity | Type | File |
|--------|------|------|
| /meta | skill | [[entities/meta]] |
| Explore | agent | [[entities/explore-agent]] |

**New**: 2 | **Updated**: 1

---

### 💡 Skill Suggestions

| Type | Name/Target | Reason |
|------|-------------|--------|
| New skill | /canvas-gen | Repeated agent pattern (2x) |
| Improve | /meta | Missing "quick mode" capability |

**Actions**: `/skill-farm create canvas-gen` | `/skill-farm improve meta`

---

### 📋 Issues Created
- meta-xxx: [SKILL] skillname: fix description
- meta-yyy: [NEW SKILL] proposal

Ready for → next session | /skill-farm | entities viewable in Obsidian

AX Layer (Agent-Parseable)

<!-- retro:state
session_id: "..."
summary: {skills_used: X, agents_spawned: Y, friction_events: Z, entities_extracted: N}
collected_data: {
  skills: [...],
  agents: [...],
  friction: [...]
}
skill_evaluation: {
  gaps: [{description, evidence, suggested_skill, priority}],
  friction: [{skill_name, issue, suggested_fix}],
  coverage: [{skill_name, missing_capability, use_case}]
}
agent_evaluation: {
  skill_needs: [{agent_type, needed_skill, rationale}],
  missing_agents: [{description, use_case, skills_needed}],
  orchestration: [{current_pattern, improvement}]
}
entities_extracted: [
  {type: "skill", name: "/meta", file: "entities/meta.md"},
  {type: "agent", name: "Explore", file: "entities/explore-agent.md"}
]
skill_suggestions: [
  {type: "new_skill", name: "canvas-gen", reason: "Repeated agent pattern", command: "/skill-farm create canvas-gen"},
  {type: "improve_skill", skill: "meta", capability: "quick mode", command: "/skill-farm improve meta"}
]
issues_created: ["meta-xxx", "meta-yyy"]
handoff: {
  skill_farm: {applicable: true, issues: [...], suggestions: [...]},
  entities: {created: [...], updated: [...]}
}
/retro:state -->

---

Agent Execution Guide

1. PARSE input and flags:
   IF --quick flag → minimal_prompts = true
   IF --verbose flag → show_raw_data = true
   IF <issue-id> provided → scope = specific_issue
   ELSE → scope = full_session

2. COLLECT session data:
   2.1 COLLECT_SKILLS():
       skills = []

       # Pattern 1: Command tags
       FOR EACH match of /<command-name>\/(\w+)<\/command-name>/ in conversation:
           skills.push({skill_name: match[1], source: "command"})

       # Pattern 2: State blocks
       FOR EACH match of /<!-- (\w+):state[\s\S]*?\/\1:state -->/:
           skill = skills.find(s => s.skill_name == match[1])
           IF skill:
               skill.outcome = PARSE state for "met: true" → "success" ELSE "partial"
           ELSE:
               skills.push({skill_name: match[1], outcome: "success", source: "state_block"})

       RETURN deduplicate(skills)

   2.2 COLLECT_AGENTS():
       agents = []

       FOR EACH Task tool call in conversation:
           agents.push({
               subagent_type: EXTRACT subagent_type param,
               description: EXTRACT description param,
               outcome: IF result message exists → "success" ELSE "unknown"
           })

       RETURN agents

   2.3 COLLECT_FRICTION():
       friction = []

       # Look for error patterns
       FOR EACH tool error or retry pattern:
           friction.push({
               tool: identified_tool,
               error_type: categorize_error(),
               recovery: detect_recovery_action()
           })

       RETURN friction

3. EVALUATE skills:
   3.1 ANALYZE_GAPS():
       gaps = []

       # Friction with workaround = potential skill gap
       FOR EACH f IN friction WHERE f.recovery == "workaround":
           gaps.push({
               description: f.context,
               evidence: [f],
               suggested_skill: INFER from workaround pattern,
               priority: "medium"
           })

       RETURN gaps

   3.2 ANALYZE_FRICTION():
       skill_friction = []

       FOR EACH skill IN skills WHERE skill.outcome != "success":
           skill_friction.push({
               skill_name: skill.skill_name,
               issue: INFER from friction_signals or outcome,
               suggested_fix: GENERATE improvement suggestion
           })

       RETURN skill_friction

   3.3 ANALYZE_COVERAGE():
       coverage = []

       # Agent work that could be a skill
       FOR EACH agent IN agents WHERE agent.description matches repeatable pattern:
           coverage.push({
               skill_name: INFER target skill,
               missing_capability: agent.description,
               use_case: INFER from context
           })

       RETURN coverage

4. EVALUATE agents:
   4.1 ANALYZE_SKILL_NEEDS():
       needs = []

       FOR EACH agent failure or limitation:
           needs.push({
               agent_type: agent.subagent_type,
               needed_skill: INFER from failure context,
               rationale: "Would have helped with X"
           })

       RETURN needs

   4.2 ANALYZE_MISSING_AGENTS():
       missing = []

       # Repeated patterns that could be specialized agents
       FOR EACH repeated_pattern IN agent_descriptions:
           missing.push({
               description: "Specialized agent for " + pattern,
               use_case: pattern_context,
               skills_needed: INFER required tools
           })

       RETURN missing

   4.3 ANALYZE_ORCHESTRATION():
       orchestration = []

       # Sequential work that could be parallel
       IF detected sequential agents without dependencies:
           orchestration.push({
               current_pattern: "Sequential execution",
               improvement: "Parallel execution possible"
           })

       RETURN orchestration

5. PROMPT user (unless --quick):
   IF NOT minimal_prompts:
       AskUserQuestion([
           {
               question: "Any additional observations about what worked well?",
               header: "Worked",
               options: [
                   {label: "Skills were helpful", description: "The skills invoked did their job"},
                   {label: "Agents were effective", description: "Subagents completed work successfully"},
                   {label: "Smooth workflow", description: "No significant friction"},
                   {label: "Nothing to add", description: "Auto-collected data is sufficient"}
               ],
               multiSelect: true
           },
           {
               question: "Any friction points the system missed?",
               header: "Friction",
               options: [
                   {label: "Skill was confusing", description: "Had to re-read or retry"},
                   {label: "Missing capability", description: "Wanted something that doesn't exist"},
                   {label: "Agent failed", description: "Subagent didn't complete as expected"},
                   {label: "None missed", description: "Auto-collection captured everything"}
               ],
               multiSelect: true
           }
       ])

       INCORPORATE user_responses into evaluation

6. EXTRACT and PERSIST entities:
   entities = EXTRACT_ENTITIES()  # See Entity Extraction section
   PERSIST_ENTITIES(entities)      # Write to obsidian-vault/entities/

   # Track what was extracted
   entities_created = []
   entities_updated = []
   FOR EACH entity IN entities:
       IF entity was new:
           entities_created.push(entity.name)
       ELSE:
           entities_updated.push(entity.name)

7. CREATE beads issues:
   issues_created = []

   # High/medium priority gaps → new skill issues
   FOR EACH gap IN gaps WHERE gap.priority IN ["high", "medium"]:
       priority_num = IF gap.priority == "high" THEN 1 ELSE 2
       result = Bash("bd create --title='[NEW SKILL] {gap.suggested_skill}' --type=feature --priority={priority_num}")
       issues_created.push(EXTRACT issue_id from result)

   # Friction fixes → improvement issues
   FOR EACH friction IN skill_friction:
       result = Bash("bd create --title='[SKILL] {friction.skill_name}: {friction.suggested_fix}' --type=task --priority=2")
       issues_created.push(EXTRACT issue_id from result)

   # Agent needs → agent issues
   FOR EACH need IN skill_needs:
       result = Bash("bd create --title='[AGENT] {need.agent_type}: add {need.needed_skill}' --type=task --priority=3")
       issues_created.push(EXTRACT issue_id from result)

9. GENERATE output:
   9.1 OUTPUT UX layer:
       - Session summary with counts
       - What worked / What didn't (auto + user)
       - Skill evaluation tables
       - Agent evaluation tables
       - Entities extracted section
       - Issues created list

   9.2 OUTPUT AX layer:
       <!-- retro:state ... -->

10. CLEAR check:
    OUTPUT "📋 CLEAR: C✓ L✓ E✓ A✓ R✓ | Retro complete"

    IF issues_created.length > 0:
        OUTPUT "→ /skill-farm can process {issues_created.length} improvement issues"

    IF entities_created.length > 0:
        OUTPUT "→ {entities_created.length} new entities in obsidian-vault/entities/"

11. ERROR recovery:
   IF collection fails → WARN, proceed with partial data
   IF bd create fails → WARN, output markdown-only (no persistence)
   IF no data collected → prompt user for manual input

---

Integration Notes

Downstream: /skill-farm

Improvement issues feed into /skill-farm:

/retro → [SKILL] issues → /skill-farm → improved skills

Auto Skill Suggestions

/retro automatically detects patterns that indicate new skills should be created:

Pattern	Detection	Suggested Action
Repeated workaround	Same friction type 2+ times	New skill to automate
Agent doing skill work	Agent description matches skill pattern	Formalize as skill
Manual multi-step	User did 3+ steps that could be one command	New skill to bundle
Coverage gap	Existing skill lacks needed capability	Skill improvement

Detection Logic:

SUGGEST_SKILLS():
    suggestions = []

    # Pattern 1: Repeated workarounds → new skill
    workaround_groups = GROUP friction BY context_similarity
    FOR EACH group WHERE group.count >= 2:
        suggestions.push({
            type: "new_skill",
            name: INFER from workaround pattern,
            reason: "Repeated workaround detected ({group.count}x)",
            evidence: group.items
        })

    # Pattern 2: Agent work → formalize as skill
    FOR EACH agent IN agents:
        IF agent.description matches /implement|create|generate|validate/:
            IF similar_agent_spawned_before(agent):
                suggestions.push({
                    type: "new_skill",
                    name: INFER from agent.description,
                    reason: "Repeated agent pattern could be a skill",
                    evidence: [agent]
                })

    # Pattern 3: Coverage gaps
    FOR EACH friction IN skill_friction:
        IF friction.issue indicates "missing capability":
            suggestions.push({
                type: "improve_skill",
                skill: friction.skill_name,
                capability: friction.issue,
                reason: "Skill coverage gap"
            })

    RETURN suggestions

Output Section:

### 💡 Skill Suggestions

| Type | Name/Target | Reason | Action |
|------|-------------|--------|--------|
| New skill | /canvas-gen | Repeated agent pattern | `/skill-farm create canvas-gen` |
| Improve | /meta | Missing "quick mode" | `/skill-farm improve meta` |

Run suggested commands or dismiss: [Create All] [Skip]

---

When to Run /retro

Good times to run:

• End of work session
• After significant friction encountered
• Want to capture patterns for reuse
• Closing significant beads issues

Skip when:

• Trivial single-step tasks
• Pure exploration with no execution

---

Examples

Example 1: After Significant Work

/retro

## Retro: Export Feature Implementation

**Session Summary**: 2 skills | 3 agents | 1 friction event

### 🎯 What Worked
- /meta analysis helped choose approach
- Parallel agent execution saved time (tasks 2 & 3)

### ⚠️ What Didn't Work
- Agent prompt was too vague, needed retry

### 🛠️ Skill Evaluation

#### Friction
| Skill | Issue | Suggested Fix |
|-------|-------|---------------|
| - | Agent prompt unclear | Add prompt templates |

### 📋 Issues Created
- meta-abc: [SKILL] Add agent prompt templates

📋 CLEAR: C✓ L✓ E✓ A✓ R✓ | Retro complete

Example 2: Quick Mode

/retro --quick

## Retro: Session Summary

**Auto-collected**: 2 skills | 1 agent | 0 friction

No significant issues detected. Session was smooth.

📋 CLEAR: C✓ L✓ E✓ A✓ R✓

---

Anti-Patterns

Don't	Do Instead
Run after every tiny task	Reserve for significant work
Skip issue creation	Persist actionable improvements
Ignore agent evaluation	Both skills AND agents matter
Over-prompt the user	Auto-collect first, ask only if needed

---

Error Handling

Condition	Recovery
No session data found	Prompt user for manual reflection
bd create fails	Output markdown, skip persistence
Collection incomplete	Proceed with partial data, note gaps
User skips all prompts	Generate from auto-collected only

---

CLEAR Check (Required Exit)

Every /retro invocation ends with:

📋 CLEAR: C✓ L✓ E✓ A✓ R✓ | Retro complete

If issues were created:

→ /skill-farm can process N improvement issues

---

Version History

Version	Date	Changes
2.0.0	2026-01-26	Major rewrite: skill-centric + agent-centric, auto-collection, beads issues
1.0.0	2026-01-21	MVP: list closed, reflect, adaptive prompts, dual storage