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Adolf is a fork/vendored clone of github.com/openclaw/openclaw (v2026.6.11), free to diverge. Tree copied sans upstream .git; upstream remote added for future syncs. Node pinned to 24 (.nvmrc); engines already require >=22.19. Preserves docs/ARCHITECTURE.md. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2
244 lines
6.6 KiB
Plaintext
244 lines
6.6 KiB
Plaintext
# VM Improver
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# Analyzes inspection reports and proposes improvements to the OpenProse VM
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#
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# Usage:
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# prose run @openprose/lib/vm-improver
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#
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# Inputs:
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# inspection_path: Path to inspection binding (e.g., .prose/runs/.../bindings/inspection.md)
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# prose_repo: Path to prose submodule (default: current project's prose/)
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#
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# Output: One or more PRs to the prose repo, or proposals if no git access
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input inspection_path: "Path to inspection output (bindings/inspection.md)"
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input prose_repo: "Path to prose skill directory (e.g., prose/skills/open-prose)"
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# ============================================================
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# Agents
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# ============================================================
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agent analyst:
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model: opus
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prompt: """
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You analyze OpenProse inspection reports for VM improvement opportunities.
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Look for evidence of:
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- Execution inefficiencies (too many steps, redundant spawns)
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- Context bloat (VM passing full values instead of references)
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- State management issues (missing bindings, path errors)
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- Error handling gaps (uncaught failures, unclear errors)
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- Missing features that would help this class of program
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- Spec ambiguities that led to incorrect execution
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Be concrete. Reference specific inspection findings.
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"""
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agent researcher:
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model: sonnet
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prompt: """
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You explore the OpenProse VM codebase to understand how to fix issues.
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Read files, understand structure, find the right places to change.
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"""
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agent implementer:
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model: opus
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prompt: """
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You implement improvements to the OpenProse VM.
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Rules:
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- Follow existing style exactly
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- Make minimal, focused changes
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- One logical change per PR
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- Update all affected files (spec, state backends, etc.)
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"""
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agent pr_author:
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model: sonnet
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prompt: """
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You create branches and pull requests with clear descriptions.
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Explain the problem, the solution, and how to test it.
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"""
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# ============================================================
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# Phase 1: Analyze Inspection for VM Issues
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# ============================================================
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let analysis = session: analyst
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prompt: """
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Read the inspection report and identify VM improvement opportunities.
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Inspection: {inspection_path}
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For each opportunity, specify:
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- category: efficiency | context | state | error | feature | spec
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- description: what's wrong
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- severity: low | medium | high
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- evidence: quote from inspection that shows this
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- hypothesis: what VM behavior likely caused this
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Return JSON:
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{
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"target_run": "run ID that was inspected",
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"opportunities": [...],
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"priority_order": [indices sorted by impact]
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}
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If the inspection shows clean execution with no issues, return empty opportunities.
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"""
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context: inspection_path
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if **no actionable opportunities found**:
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output result = {
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status: "no-improvements-needed",
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analysis: analysis,
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message: "Inspection showed clean VM execution"
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}
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# ============================================================
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# Phase 2: Research VM Codebase
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# ============================================================
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let research = session: researcher
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prompt: """
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For each opportunity, find the relevant VM code.
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Prose repo: {prose_repo}
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Opportunities: {analysis.opportunities}
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Key files to check:
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- prose.md (main VM spec)
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- state/filesystem.md, state/sqlite.md, state/postgres.md
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- primitives/session.md
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- compiler.md
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- SKILL.md
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Return JSON:
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{
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"findings": [
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{
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"opportunity_index": N,
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"relevant_files": ["path/to/file.md"],
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"current_behavior": "how it works now",
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"change_location": "specific section or line range"
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}
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]
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}
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"""
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context: { analysis, prose_repo }
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# ============================================================
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# Phase 3: User Selection
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# ============================================================
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input selection: """
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## VM Improvement Opportunities
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Based on inspection of: {analysis.target_run}
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### Opportunities Found:
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{analysis.opportunities}
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### Research:
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{research.findings}
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---
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Which improvements should I implement as PRs?
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- List by number (e.g., "1, 3")
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- Or "all" for everything
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- Or "none" to skip
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"""
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if **user selected none or wants to skip**:
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output result = {
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status: "skipped",
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analysis: analysis,
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research: research
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}
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let selected = session "Parse selection"
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prompt: "Extract selected opportunity indices from user input"
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context: { selection, analysis }
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# ============================================================
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# Phase 4: Implement Changes
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# ============================================================
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let implementations = selected | map:
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session: implementer
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prompt: """
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Implement this VM improvement.
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Opportunity: {analysis.opportunities[item]}
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Research: {research.findings[item]}
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Prose repo: {prose_repo}
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1. Read the current file content
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2. Design the minimal change
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3. Write the improved content
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Return JSON:
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{
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"opportunity_index": N,
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"branch_name": "vm/short-description",
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"title": "PR title",
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"files": [
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{
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"path": "relative/path.md",
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"action": "modify",
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"description": "what changed"
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}
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],
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"summary": "2-3 sentence explanation"
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}
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Actually write the changes to the files.
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"""
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context: item
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permissions:
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read: ["{prose_repo}/**"]
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write: ["{prose_repo}/**"]
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# ============================================================
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# Phase 5: Create PRs
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# ============================================================
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let prs = implementations | map:
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session: pr_author
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prompt: """
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Create a PR for this VM improvement.
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Implementation: {item}
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Prose repo: {prose_repo}
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Steps:
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1. cd to prose repo
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2. Create branch: {item.branch_name}
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3. Stage changed files
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4. Commit with clear message
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5. Push branch
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6. Create PR via gh cli
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PR body should include:
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- Problem: what inspection revealed
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- Solution: what this changes
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- Testing: how to verify
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Return: { pr_url, branch, title }
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"""
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context: item
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permissions:
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bash: allow
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# ============================================================
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# Output
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# ============================================================
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output result = {
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status: "complete",
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target_run: analysis.target_run,
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opportunities_found: analysis.opportunities,
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opportunities_implemented: implementations,
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prs_created: prs
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}
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