clawdbot/extensions/open-prose/skills/prose/lib

OpenProse Standard Library

Core programs that ship with OpenProse. Production-quality, well-tested programs for common tasks.

Programs

Evaluation & Improvement

Program	Description
inspector.prose	Post-run analysis for runtime fidelity and task effectiveness
vm-improver.prose	Analyzes inspections and proposes PRs to improve the VM
program-improver.prose	Analyzes inspections and proposes PRs to improve .prose source
cost-analyzer.prose	Token usage and cost pattern analysis
calibrator.prose	Validates light evaluations against deep evaluations
error-forensics.prose	Root cause analysis for failed runs

Memory

Program	Description
user-memory.prose	Cross-project persistent personal memory
project-memory.prose	Project-scoped institutional memory

The Improvement Loop

The evaluation programs form a recursive improvement cycle:

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   Run Program  ──►  Inspector  ──►  VM Improver ──► PR     │
│        ▲                │                                   │
│        │                ▼                                   │
│        │         Program Improver ──► PR                    │
│        │                │                                   │
│        └────────────────┘                                   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Supporting analysis:

• cost-analyzer — Where does the money go? Optimization opportunities.
• calibrator — Are cheap evaluations reliable proxies for expensive ones?
• error-forensics — Why did a run fail? Root cause analysis.

Usage

# Inspect a completed run
prose run lib/inspector.prose
# Inputs: run_path, depth (light|deep), target (vm|task|all)

# Propose VM improvements
prose run lib/vm-improver.prose
# Inputs: inspection_path, prose_repo

# Propose program improvements
prose run lib/program-improver.prose
# Inputs: inspection_path, run_path

# Analyze costs
prose run lib/cost-analyzer.prose
# Inputs: run_path, scope (single|compare|trend)

# Validate light vs deep evaluation
prose run lib/calibrator.prose
# Inputs: run_paths, sample_size

# Investigate failures
prose run lib/error-forensics.prose
# Inputs: run_path, focus (vm|program|context|external)

# Memory programs (recommend sqlite+ backend)
prose run lib/user-memory.prose --backend sqlite+
# Inputs: mode (teach|query|reflect), content

prose run lib/project-memory.prose --backend sqlite+
# Inputs: mode (ingest|query|update|summarize), content

Memory Programs

The memory programs use persistent agents to accumulate knowledge:

user-memory (persist: user)

• Learns your preferences, decisions, patterns across all projects
• Remembers mistakes and lessons learned
• Answers questions from accumulated knowledge

project-memory (persist: project)

• Understands this project's architecture and decisions
• Tracks why things are the way they are
• Answers questions with project-specific context

Both recommend --backend sqlite+ for durable persistence.

Design Principles

1. Production-ready — Tested, documented, handles edge cases
2. Composable — Can be imported via use in other programs
3. User-scoped state — Cross-project utilities use persist: user
4. Minimal dependencies — No external services required
5. Clear contracts — Well-defined inputs and outputs
6. Incremental value — Useful in simple mode, more powerful with depth