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clawdbot/extensions/open-prose/skills/prose/lib/vm-improver.prose
2026-01-23 00:49:40 +00:00

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# VM Improver
# Analyzes inspection reports and proposes improvements to the OpenProse VM
#
# Usage:
# prose run @openprose/lib/vm-improver
#
# Inputs:
# inspection_path: Path to inspection binding (e.g., .prose/runs/.../bindings/inspection.md)
# prose_repo: Path to prose submodule (default: current project's prose/)
#
# Output: One or more PRs to the prose repo, or proposals if no git access
input inspection_path: "Path to inspection output (bindings/inspection.md)"
input prose_repo: "Path to prose skill directory (e.g., prose/skills/open-prose)"
# ============================================================
# Agents
# ============================================================
agent analyst:
model: opus
prompt: """
You analyze OpenProse inspection reports for VM improvement opportunities.
Look for evidence of:
- Execution inefficiencies (too many steps, redundant spawns)
- Context bloat (VM passing full values instead of references)
- State management issues (missing bindings, path errors)
- Error handling gaps (uncaught failures, unclear errors)
- Missing features that would help this class of program
- Spec ambiguities that led to incorrect execution
Be concrete. Reference specific inspection findings.
"""
agent researcher:
model: sonnet
prompt: """
You explore the OpenProse VM codebase to understand how to fix issues.
Read files, understand structure, find the right places to change.
"""
agent implementer:
model: opus
prompt: """
You implement improvements to the OpenProse VM.
Rules:
- Follow existing style exactly
- Make minimal, focused changes
- One logical change per PR
- Update all affected files (spec, state backends, etc.)
"""
agent pr_author:
model: sonnet
prompt: """
You create branches and pull requests with clear descriptions.
Explain the problem, the solution, and how to test it.
"""
# ============================================================
# Phase 1: Analyze Inspection for VM Issues
# ============================================================
let analysis = session: analyst
prompt: """
Read the inspection report and identify VM improvement opportunities.
Inspection: {inspection_path}
For each opportunity, specify:
- category: efficiency | context | state | error | feature | spec
- description: what's wrong
- severity: low | medium | high
- evidence: quote from inspection that shows this
- hypothesis: what VM behavior likely caused this
Return JSON:
{
"target_run": "run ID that was inspected",
"opportunities": [...],
"priority_order": [indices sorted by impact]
}
If the inspection shows clean execution with no issues, return empty opportunities.
"""
context: inspection_path
if **no actionable opportunities found**:
output result = {
status: "no-improvements-needed",
analysis: analysis,
message: "Inspection showed clean VM execution"
}
# ============================================================
# Phase 2: Research VM Codebase
# ============================================================
let research = session: researcher
prompt: """
For each opportunity, find the relevant VM code.
Prose repo: {prose_repo}
Opportunities: {analysis.opportunities}
Key files to check:
- prose.md (main VM spec)
- state/filesystem.md, state/sqlite.md, state/postgres.md
- primitives/session.md
- compiler.md
- SKILL.md
Return JSON:
{
"findings": [
{
"opportunity_index": N,
"relevant_files": ["path/to/file.md"],
"current_behavior": "how it works now",
"change_location": "specific section or line range"
}
]
}
"""
context: { analysis, prose_repo }
# ============================================================
# Phase 3: User Selection
# ============================================================
input selection: """
## VM Improvement Opportunities
Based on inspection of: {analysis.target_run}
### Opportunities Found:
{analysis.opportunities}
### Research:
{research.findings}
---
Which improvements should I implement as PRs?
- List by number (e.g., "1, 3")
- Or "all" for everything
- Or "none" to skip
"""
if **user selected none or wants to skip**:
output result = {
status: "skipped",
analysis: analysis,
research: research
}
let selected = session "Parse selection"
prompt: "Extract selected opportunity indices from user input"
context: { selection, analysis }
# ============================================================
# Phase 4: Implement Changes
# ============================================================
let implementations = selected | map:
session: implementer
prompt: """
Implement this VM improvement.
Opportunity: {analysis.opportunities[item]}
Research: {research.findings[item]}
Prose repo: {prose_repo}
1. Read the current file content
2. Design the minimal change
3. Write the improved content
Return JSON:
{
"opportunity_index": N,
"branch_name": "vm/short-description",
"title": "PR title",
"files": [
{
"path": "relative/path.md",
"action": "modify",
"description": "what changed"
}
],
"summary": "2-3 sentence explanation"
}
Actually write the changes to the files.
"""
context: item
permissions:
read: ["{prose_repo}/**"]
write: ["{prose_repo}/**"]
# ============================================================
# Phase 5: Create PRs
# ============================================================
let prs = implementations | map:
session: pr_author
prompt: """
Create a PR for this VM improvement.
Implementation: {item}
Prose repo: {prose_repo}
Steps:
1. cd to prose repo
2. Create branch: {item.branch_name}
3. Stage changed files
4. Commit with clear message
5. Push branch
6. Create PR via gh cli
PR body should include:
- Problem: what inspection revealed
- Solution: what this changes
- Testing: how to verify
Return: { pr_url, branch, title }
"""
context: item
permissions:
bash: allow
# ============================================================
# Output
# ============================================================
output result = {
status: "complete",
target_run: analysis.target_run,
opportunities_found: analysis.opportunities,
opportunities_implemented: implementations,
prs_created: prs
}