/melt - Task-Agnostic Autonomous Execution
Complete guide to the /melt skill for autonomous task execution with optional Agent Teams planning and completion validation. (/build is a legacy alias.)
Table of Contents
- Overview
- When to Use
- Planning
- Workflow
- Completion Checkpoint
- Comparison with /appfix
- Skill Fluidity
- Troubleshooting
Overview
/melt is the universal autonomous execution skill. It provides:
- Optional Agent Teams Planning - Encouraged for complex tasks, not mandated
- 100% Autonomous Operation - No permission prompts, no confirmation requests
- Completion Checkpoint - Deterministic boolean validation before stopping
- Browser Verification - Mandatory testing in real browser
- Strict Linter Policy - Fix ALL errors, including pre-existing ones
Key Features
| Feature | Description |
|---|---|
| Agent Teams planning | Encouraged for complex tasks via TeamCreate |
| Auto-approval hooks | All tool permissions granted automatically |
| Stop hook validation | Cannot stop until checkpoint booleans pass |
| Checkpoint invalidation | Stale fields reset when code changes |
| Version tracking | Detects code drift since checkpoints were set |
When to Use
Use /melt
| Scenario | Example |
|---|---|
| Feature implementation | "Add a logout button to the navbar" |
| Bug fixes | "Fix the broken pagination" |
| Refactoring | "Convert class components to hooks" |
| Config changes | "Update the API endpoint URLs" |
| Any task requiring completion verification | "Deploy the new feature" |
Use /repair Instead
| Scenario | Why repair? |
|---|---|
| Production/staging is down | Routes to /appfix with health check phases |
| Debugging failures | Routes to /appfix with log collection phases |
| Mobile app crashes | Routes to /mobileappfix with Maestro tests |
Planning
For complex tasks, spawn an Agent Team to ensure optimal implementation planning. Use TeamCreate to coordinate parallel agents, or launch parallel Task() calls directly.
Recommended Agents (3-5)
| Agent | Role | Key Question |
|---|---|---|
| First Principles | Simplification | "What can be deleted? What's over-engineered?" |
| AGI-Pilled | Capability | "What would god-tier AI implementation look like?" |
| Task-specific experts | Domain knowledge | 1-3 additional agents tailored to the problem |
Important: Forge reads the core agent prompts from ~/.claude/skills/0-heavy/SKILL.md at runtime. This ensures prompts stay in sync - when heavy improves, forge automatically benefits.
Why Agent Teams?
| Without Planning | With Agent Teams |
|---|---|
| Over-engineering | First Principles asks "delete this?" |
| Under-ambition | AGI-Pilled asks "why constrain the model?" |
| Scope creep | First Principles enforces simplicity |
| Conservative design | AGI-Pilled pushes for intelligence-maximizing |
Synthesis Output
After agents return, synthesize their insights:
TRADEOFF: [topic]
- First Principles: Delete X because [reason]
- AGI-Pilled: Expand Y because [capability argument]
- Resolution: [chosen approach with rationale]
Workflow
Phase 0: Activation
# The skill creates the state file on activation
mkdir -p .claude && cat > .claude/autonomous-state.json << 'EOF'
{"mode": "melt", "started_at": "2025-01-26T10:00:00Z", "task": "user task"}
EOF
mkdir -p ~/.claude && cat > ~/.claude/autonomous-state.json << 'EOF'
{"mode": "melt", "started_at": "2025-01-26T10:00:00Z", "origin_project": "/path/to/project"}
EOF
Phase 0.5: Planning
Encouraged for complex tasks before making any changes.
EnterPlanMode- Switch to planning mode- Explore the codebase:
- Project structure and architecture
- Recent commits:
git log --oneline -15 - Environment and deployment configs
- Relevant code patterns for the task
- Existing tests and validation
- Spawn Agent Team via
TeamCreateor parallelTask()calls (First Principles + AGI-Pilled + task-specific experts, 3-5 recommended) - Synthesize tradeoffs and write to plan file
ExitPlanMode- Get plan approved
Why this matters: Jumping straight to code leads to broken functionality, inconsistent patterns, and wasted effort.
Phase 1: Execute
- Make code changes (Edit tool)
- Run linters, fix ALL errors
- Commit and push
- Deploy if needed
Phase 2: Verify (MANDATORY)
CRITICAL: Use Surf CLI first, not Chrome MCP.
- Run Surf CLI:
python3 ~/.claude/hooks/surf-verify.py --urls "https://..." - Check
.claude/web-smoke/summary.jsonfor pass/fail - Only fall back to Chrome MCP if Surf CLI unavailable
- Check console for errors
- Verify feature works as expected
Phase 3: Complete
- Update completion checkpoint
- Try to stop
- If blocked: address issues, try again
- If passed: clean up state files
Completion Checkpoint
Create .claude/completion-checkpoint.json:
{
"self_report": {
"is_job_complete": true,
"code_changes_made": true,
"linters_pass": true,
"category": "refactor"
},
"reflection": {
"what_was_done": "Implemented feature X, deployed to staging, verified in browser",
"what_remains": "none",
"key_insight": "Reusable lesson for future sessions (>50 chars)",
"search_terms": ["keyword1", "keyword2"],
"memory_that_helped": []
}
}
Required Fields
| Field | Type | Requirement |
|---|---|---|
is_job_complete |
boolean | Is the task fully done? |
code_changes_made |
boolean | Did you modify code? |
linters_pass |
boolean | Do all linters pass? |
category |
enum | bugfix, gotcha, architecture, pattern, config, refactor |
what_was_done |
string | >20 chars describing work completed |
what_remains |
string | Must be "none" to pass |
key_insight |
string | >50 chars, reusable lesson for future sessions |
search_terms |
array | 2-7 keywords for future discovery |
memory_that_helped |
array | Optional, memories that aided this task |
Comparison with /appfix
| Aspect | /melt | /appfix |
|---|---|---|
| Purpose | Any task | Debugging failures |
| Agent Teams planning | Encouraged | No |
| docs_read_at_start | Not required | Required |
| Health check phase | No | Yes |
| Log collection | No | Yes |
| Service topology | Not required | Required |
| Linter policy | Strict | Strict |
| Browser verification | Required | Required |
| Checkpoint schema | Same | Same |
/melt is the universal base with optional Agent Teams planning. /appfix adds debugging-specific phases.
Skill Fluidity
Skills are capabilities, not cages. You may use techniques from any skill for sub-problems without switching modes.
Troubleshooting
"Checkpoint validation failed"
Your checkpoint has incomplete fields. Check:
is_job_complete- Are you honestly done?linters_pass- Did all linters pass?what_remains- Is it"none"?key_insight- Is it >50 chars?
"Stop hook blocked me"
This is expected when work is incomplete:
- If
is_job_complete: false→ you're blocked - Complete the work, update checkpoint, try again
"linters_pass is false"
Fix ALL linter errors:
# JavaScript/TypeScript
npm run lint && npx tsc --noEmit
# Python
ruff check --fix . && pyright
"These errors aren't related to our code" is NOT acceptable.
State Files
| File | Purpose |
|---|---|
.claude/autonomous-state.json |
Enables auto-approval hooks ("mode": "melt") |
.claude/completion-checkpoint.json |
Boolean self-report for validation |
~/.claude/autonomous-state.json |
User-level state for cross-repo work |
Cleanup
Remove state files when done:
rm -f ~/.claude/autonomous-state.json .claude/autonomous-state.json