retrospective

name	retrospective
description	Analyze the current session for improvement opportunities in skills, memory, and workflow. Spawns a sub-agent for unbiased analysis. Use when a session involved complex problem-solving, error recovery, user corrections, or multi-step workflows. Trigger on "/retrospective" command or at session end after significant work.

Retrospective -- Session Analysis & Improvement

When to use

• After a complex session (5+ tool calls, error recovery, multi-step workflow)
• When the user corrected your approach ("no, do it this way")
• After a failed attempt that required a different strategy
• Before context window exhaustion on a productive session
• Explicitly via /retrospective command

Arguments

Argument	Required	Description
scope	no	What to analyze: session (default), last-task, last-hour
focus	no	Narrow analysis: skills, memory, workflow, or all (default)

Examples:

• /retrospective -- full session analysis
• /retrospective scope=last-task focus=skills -- only skill improvements for the last task
• /retrospective focus=memory -- only memory tier/content suggestions

Procedure

1. Gather session data

Collect from the current conversation context:

• What tasks were attempted
• Which approaches succeeded vs failed
• User corrections and their reasoning
• Tools/skills used and their effectiveness
• Errors encountered and how they were resolved
• Time spent on dead ends vs productive work

Also pull external state:

AGENT_ID="$(echo $BOT_NAME | tr '[:upper:]' '[:lower:]')"

# Recent memories written this session
curl -s -H "Authorization: Bearer $(cat store/.dashboard-token)" \
  "http://localhost:3420/api/memories?agent=$AGENT_ID&category=hot&limit=20"

# Today's daily log entries
DATE=$(date +%Y-%m-%d)
curl -s -H "Authorization: Bearer $(cat store/.dashboard-token)" \
  "http://localhost:3420/api/daily-log?agent=$AGENT_ID&date=$DATE"

# Skills that were referenced or used
ls ~/.claude/skills/ | head -30

2. Analyze with A/B/C framework

For each significant event in the session, evaluate:

A -- What happened? State the fact: what was attempted, what was the outcome.

B -- Why did it happen that way? Root cause: was it a missing skill, wrong memory tier, incorrect assumption, tooling gap, or communication issue?

C -- What should change? Concrete action: new skill, skill patch, memory write/update, workflow change, or nothing (if the outcome was correct).

3. Generate improvement proposals

Organize findings into categories:

Skill proposals

For each skill-related finding:

SKILL_ACTION: create | patch | delete
SKILL_NAME: name-of-skill
REASON: why this change helps
CHANGE: what specifically to add/modify/remove

Rules:

• Only propose a NEW skill if the pattern appeared 2+ times or was complex enough (5+ steps)
• Prefer PATCH over CREATE -- check if an existing skill covers 80% of the case
• Check ~/.claude/skills/.skill-index.md before proposing duplicates

Memory proposals

For each memory-related finding:

MEMORY_ACTION: save | update | delete | retier
MEMORY_TIER: hot | warm | cold | shared
CONTENT: what to remember
REASON: why this tier, why now

Rules:

• User corrections -> always save as warm (stable preference)
• Task-specific findings -> hot (will decay naturally)
• Architectural decisions -> cold (long-term reference)
• Cross-agent learnings -> shared

Workflow proposals

For process improvements that don't fit skills or memory:

WORKFLOW_CHANGE: description of the process change
APPLIES_TO: this agent | all agents | specific agent
REASON: what problem it solves

4. Present to user for approval

Format the output as a concise action list:

## Retrospective Summary

Session: [brief description]
Duration: ~[estimate]
Key events: [count] tasks, [count] corrections, [count] errors

### Proposed Changes

#### Skills
1. [PATCH] skill-name: add X because Y
2. [CREATE] new-skill: handles Z pattern (seen 3x this session)

#### Memory
1. [SAVE warm] "user prefers X over Y" -- correction at [context]
2. [RETIER hot->cold] "project Z deadline" -- no longer active

#### Workflow
1. [ALL AGENTS] Always check CI before merging -- 2 failed merges this session

Apply all? (y/n/select)

5. Execute approved changes

On user approval:

• Skills: create/patch SKILL.md files, regenerate .skill-index.md
• Memory: write/update via the memory API
• Workflow: update CLAUDE.md or inter-agent message to affected agents

After execution, log the retrospective to the daily log:

curl -s -X POST -H "Authorization: Bearer $(cat store/.dashboard-token)" \
  http://localhost:3420/api/daily-log \
  -H "Content-Type: application/json" \
  -d "{\"agent_id\":\"$AGENT_ID\",\"content\":\"## $(date +%H:%M) -- Retrospective\n[count] skill changes, [count] memory updates, [count] workflow changes applied.\"}"

Pitfalls

• Do NOT auto-apply changes without user approval -- always present first
• Do NOT create skills for one-off tasks that won't repeat
• Do NOT save ephemeral debugging context as cold memory
• If the session was straightforward with no issues, say so and skip -- not every session needs changes
• Keep proposals actionable and specific -- "be better at X" is not a proposal

Relation to existing mechanisms

Mechanism	Retrospective's role
memoria-heartbeat	Retrospective is the dedicated, deeper version. Heartbeat does inline A/B/C; retrospective does it thoroughly with user approval
skill-factory	Retrospective proposes skills; skill-factory creates them. Retrospective may invoke skill-factory for CREATE actions
DREAM.md	Nightly consolidation. Retrospective is on-demand, immediate
/handoff	Retrospective analyzes; handoff transfers. Different purposes