Files
clawdbot/extensions/open-prose/skills/prose/examples/31-captains-chair-with-memory.prose
2026-01-23 00:49:40 +00:00

146 lines
4.9 KiB
Plaintext

# Captain's Chair with Memory and Self-Improvement
#
# An advanced orchestration pattern that includes:
# - Retrospective analysis after task completion
# - Learning from mistakes to improve future runs
# - Continuous critic supervision during execution
#
# From the blog post: "Future agents will flip the plan:execute paradigm
# to 80:20 from today's 20:80"
input task: "The task to accomplish"
input past_learnings: "Previous session learnings (if any)"
# ============================================================================
# Agent Definitions
# ============================================================================
agent captain:
model: opus
prompt: """You are a senior engineering manager. You coordinate but never code directly.
Your responsibilities:
1. Strategic planning with 80% of effort on planning, 20% on execution oversight
2. Dispatch specialized subagents for all implementation
3. Validate outputs meet requirements
4. Learn from each session to improve future runs
Past learnings to incorporate:
{past_learnings}"""
agent planner:
model: opus
prompt: """You are a meticulous planner. Create implementation plans with:
- Exact files and line numbers to modify
- Code patterns to follow from existing codebase
- Edge cases to handle
- Tests to write"""
agent researcher:
model: haiku
prompt: "Find specific information quickly. Cite sources."
agent executor:
model: sonnet
prompt: "Implement precisely according to plan. Follow existing patterns."
agent critic:
model: sonnet
prompt: """You are a continuous critic. Your job is to watch execution and flag:
- Deviations from plan
- Emerging issues
- Opportunities for improvement
Be proactive - don't wait for completion to raise concerns."""
agent retrospective:
model: opus
prompt: """You analyze completed sessions to extract learnings:
- What went well?
- What could be improved?
- What should be remembered for next time?
Output actionable insights, not platitudes."""
# ============================================================================
# Phase 1: Deep Planning (80% of effort)
# ============================================================================
# Parallel research - gather everything needed upfront
parallel:
codebase = session: researcher
prompt: "Map the relevant parts of the codebase for: {task}"
patterns = session: researcher
prompt: "Find coding patterns and conventions used in this repo"
docs = session: researcher
prompt: "Find documentation and prior decisions related to: {task}"
issues = session: researcher
prompt: "Find known issues, TODOs, and edge cases for: {task}"
# Create detailed implementation plan
let detailed_plan = session: planner
prompt: """Create a comprehensive implementation plan for: {task}
Use the research to specify:
1. Exact changes needed (file:line format)
2. Code patterns to follow
3. Edge cases from prior issues
4. Test coverage requirements"""
context: { codebase, patterns, docs, issues }
# Critic reviews plan BEFORE execution
let plan_critique = session: critic
prompt: "Review this plan for gaps, risks, and unclear requirements"
context: detailed_plan
# Captain decides if plan needs revision
if **plan critique identified blocking issues**:
let revised_plan = session: planner
prompt: "Revise the plan to address critique"
context: { detailed_plan, plan_critique }
else:
let revised_plan = detailed_plan
# ============================================================================
# Phase 2: Supervised Execution (20% of effort)
# ============================================================================
# Execute with concurrent critic supervision
parallel:
implementation = session: executor
prompt: "Implement according to the plan"
context: revised_plan
live_critique = session: critic
prompt: "Monitor implementation for deviations and emerging issues"
context: revised_plan
# Captain validates and integrates
let validated = session: captain
prompt: """Validate the implementation:
- Does it match the plan?
- Were critic's live concerns addressed?
- Is it ready for user review?"""
context: { implementation, live_critique, revised_plan }
# ============================================================================
# Phase 3: Retrospective and Learning
# ============================================================================
# Extract learnings for future sessions
let session_learnings = session: retrospective
prompt: """Analyze this completed session:
Plan: {revised_plan}
Implementation: {implementation}
Critique: {live_critique}
Validation: {validated}
Extract:
1. What patterns worked well?
2. What caused friction or rework?
3. What should the captain remember next time?
4. Any codebase insights to preserve?"""
context: { revised_plan, implementation, live_critique, validated }
# Output both the result and the learnings
output result = validated
output learnings = session_learnings