autonomous-loops

name autonomous-loops description Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility only: when new autonomous loop guidance is needed, use continuous-agent-loop instead. metadata {"origin":"ECC"} Autonomous Loops Skill Compatibility note (v1.8.0): autonomous-loops is retained for one release. The canonical skill name is now continuous-agent-loop . New loop guidance should be authored there, while this skill remains available to avoid breaking existing workflows. Patterns, architectures, and reference implementations for running Claude Code autonomously in loops. Covers everything from simple claude -p pipelines to full RFC-driven multi-agent DAG orchestration. When to Use Setting up autonomous development workflows that run without human intervention Choosing the right loop architecture for your problem (simple vs complex) Building CI/CD-style continuous development pipelines Running parallel agents with merge coordination Implementing context persistence across loop iterations Adding quality gates and cleanup passes to autonomous workflows Loop Pattern Spectrum From simplest to most sophisticated: Pattern Complexity Best For Sequential Pipeline Low Daily dev steps, scripted workflows NanoClaw REPL Low Interactive persistent sessions Infinite Agentic Loop Medium Parallel content generation, spec-driven work Continuous Claude PR Loop Medium Multi-day iterative projects with CI gates De-Sloppify Pattern Add-on Quality cleanup after any Implementer step Ralphinho / RFC-Driven DAG High Large features, multi-unit parallel work with merge queue 1. Sequential Pipeline ( claude -p ) The simplest loop. Break daily development into a sequence of non-interactive claude -p calls. Each call is a focused step with a clear prompt. Core Insight If you can't figure out a loop like this, it means you can't even drive the LLM to fix your code in interactive mode. The claude -p flag runs Claude Code non-interactively with a prompt, exits when done. Chain calls to build a pipeline: #!/bin/bash # daily-dev.sh — Sequential pipeline for a feature branch set -e # Step 1: Implement the feature claude -p "Read the spec in docs/auth-spec.md. Implement OAuth2 login in src/auth/. Write tests first (TDD). Do NOT create any new documentation files." # Step 2: De-sloppify (cleanup pass) claude -p "Review all files changed by the previous commit. Remove any unnecessary type tests, overly defensive checks, or testing of language features (e.g., testing that TypeScript generics work). Keep real business logic tests. Run the test suite after cleanup." # Step 3: Verify claude -p "Run the full build, lint, type check, and test suite. Fix any failures. Do not add new features." # Step 4: Commit claude -p "Create a conventional commit for all staged changes. Use 'feat: add OAuth2 login flow' as the message." Key Design Principles Each step is isolated — A fresh context window per claude -p call means no context bleed between steps. Order matters — Steps execute sequentially. Each builds on the filesystem state left by the previous. Negative instructions are dangerous — Don't say "don't test type systems." Instead, add a separate cleanup step (see De-Sloppify Pattern ). Exit codes propagate — set -e stops the pipeline on failure. Variations With model routing: # Research with Opus (deep reasoning) claude -p --model opus "Analyze the codebase architecture and write a plan for adding caching..." # Implement with Sonnet (fast, capable) claude -p "Implement the caching layer according to the plan in docs/caching-plan.md..." # Review with Opus (thorough) claude -p --model opus "Review all changes for security issues, race conditions, and edge cases..." With environment context: # Pass context via files, not prompt length echo "Focus areas: auth module, API rate limiting" > .claude-context.md claude -p "Read .claude-context.md for priorities. Work through them in order." rm .claude-context.md With --allowedTools restrictions: # Read-only analysis pass claude -p --allowedTools "Read,Grep,Glob" "Audit this codebase for security vulnerabilities..." # Write-only implementation pass claude -p --allowedTools "Read,Write,Edit,Bash" "Implement the fixes from security-audit.md..." 2. NanoClaw REPL ECC's built-in persistent loop. A session-aware REPL that calls claude -p synchronously with full conversation history. # Start the default session node scripts/claw.js # Named session with skill context CLAW_SESSION=my-project CLAW_SKILLS=tdd-workflow,security-review node scripts/claw.js How It Works Loads conversation history from ~/.claude/claw/{session}.md Each user message is sent to claude -p with full history as context Responses are appended to the session file (Markdown-as-database) Sessions persist across restarts When NanoClaw vs Sequential Pipeline Use Case NanoClaw Sequential Pipeline Interactive exploration Yes No Scripted automation No Yes Session persistence Built-in Manual Context accumulation Grows per turn Fresh each step CI/CD integration Poor Excellent See the /claw command documentation for full details. 3. Infinite Agentic Loop A two-prompt system that orchestrates parallel sub-agents for specification-driven generation. Developed by disler (credit: @disler). Architecture: Two-Prompt System PROMPT 1 (Orchestrator) PROMPT 2 (Sub-Agents) ┌─────────────────────┐ ┌──────────────────────┐ │ Parse spec file │ │ Receive full context │ │ Scan output dir │ deploys │ Read assigned number │ │ Plan iteration │────────────│ Follow spec exactly │ │ Assign creative dirs │ N agents │ Generate unique output │ │ Manage waves │ │ Save to output dir │ └─────────────────────┘ └──────────────────────┘ The Pattern Spec Analysis — Orchestrator reads a specification file (Markdown) defining what to generate Directory Recon — Scans existing output to find the highest iteration number Parallel Deployment — Launches N sub-agents, each with: The full spec A unique creative direction A specific iteration number (no conflicts) A snapshot of existing iterations (for uniqueness) Wave Management — For infinite mode, deploys waves of 3-5 agents until context is exhausted Implementation via Claude Code Commands Create .claude/commands/infinite.md : Parse the following arguments from $ARGUMENTS: 1. spec _file — path to the specification markdown 2. output_ dir — where iterations are saved 3. count — integer 1-N or "infinite" PHASE 1: Read and deeply understand the specification. PHASE 2: List output _dir, find highest iteration number. Start at N+1. PHASE 3: Plan creative directions — each agent gets a DIFFERENT theme/approach. PHASE 4: Deploy sub-agents in parallel (Task tool). Each receives: - Full spec text - Current directory snapshot - Their assigned iteration number - Their unique creative direction PHASE 5 (infinite mode): Loop in waves of 3-5 until context is low. Invoke: /project:infinite specs/component-spec.md src/ 5 /project:infinite specs/component-spec.md src/ infinite Batching Strategy Count Strategy 1-5 All agents simultaneously 6-20 Batches of 5 infinite Waves of 3-5, progressive sophistication Key Insight: Uniqueness via Assignment Don't rely on agents to self-differentiate. The orchestrator assigns each agent a specific creative direction and iteration number. This prevents duplicate concepts across parallel agents. 4. Continuous Claude PR Loop A production-grade shell script that runs Claude Code in a continuous loop, creating PRs, waiting for CI, and merging automatically. Created by AnandChowdhary (credit: @AnandChowdhary). Core Loop ┌─────────────────────────────────────────────────────┐ │ CONTINUOUS CLAUDE ITERATION │ │ │ │ 1. Create branch (continuous-claude/iteration-N) │ │ 2. Run claude -p with enhanced prompt │ │ 3. (Optional) Reviewer pass — separate claude -p │ │ 4. Commit changes (claude generates message) │ │ 5. Push + create PR (gh pr create) │ │ 6. Wait for CI checks (poll gh pr checks) │ │ 7. CI failure? → Auto-fix pass (claude -p) │ │ 8. Merge PR (squash/merge/rebase) │ │ 9. Return to main → repeat │ │ │ │ Limit by: --max-runs N | --max-cost $X │ │ --max-duration 2h | completion signal │ └─────────────────────────────────────────────────────┘ Installation Warning: Install continuous-claude from its repository after reviewing the code. Do not pipe external scripts directly to bash. Usage # Basic: 10 iterations continuous-claude --prompt "Add unit tests for all untested functions" --max-runs 10 # Cost-limited continuous-claude --prompt "Fix all linter errors" --max-cost 5.00 # Time-boxed continuous-claude --prompt "Improve test coverage" --max-duration 8h # With code review pass continuous-claude \ --prompt "Add authentication feature" \ --max-runs 10 \ --review-prompt "Run npm test && npm run lint, fix any failures" # Parallel via worktrees continuous-claude --prompt "Add tests" --max-runs 5 --worktree tests-worker & continuous-claude --prompt "Refactor code" --max-runs 5 --worktree refactor-worker & wait Cross-Iteration Context: SHARED_TASK_NOTES.md The critical innovation: a SHARED_TASK_NOTES.md file persists across iterations: ## Progress - [x] Added tests for auth module (iteration 1) - [x] Fixed edge case in token refresh (iteration 2) - [ ] Still need: rate limiting tests, error boundary tests ## Next Steps - Focus on rate limiting module next - The mock setup in tests/helpers.ts can be reused Claude reads this file at iteration start and updates it at iteration end. This bridges the context gap between independent claude -p invocations. CI Failure Recovery When PR checks fail, Continuous Claude automatically: Fetches the failed run ID via gh run list Spawns a new claude -p with CI fix context Claude inspects logs via gh run view , fixes code, commits, pushes Re-waits for checks (up to --ci-retry-max attempts) Completion Signal Claude can signal "I'm done" by outputting a magic phrase: continuous-claude \ --prompt "Fix all bugs in the issue tracker" \ --completion-signal "CONTINUOUS_CLAUDE_PROJECT_COMPLETE" \ --completion-threshold 3 # Stops after 3 consecutive signals Three consecutive iterations signaling completion stops the loop, preventing wasted runs on finished work. Key Configuration Flag Purpose --max-runs N Stop after N successful iterations --max-cost $X Stop after spending $X --max-duration 2h Stop after time elapsed --merge-strategy squash squash, merge, or rebase --worktree <name> Parallel execution via git worktrees --disable-commits Dry-run mode (no git operations) --review-prompt "..." Add reviewer pass per iteration --ci-retry-max N Auto-fix CI failures (default: 1) 5. The De-Sloppify Pattern An add-on pattern for any loop. Add a dedicated cleanup/refactor step after each Implementer step. The Problem When you ask an LLM to implement with TDD, it takes "write tests" too literally: Tests that verify TypeScript's type system works (testing typeof x === 'string' ) Overly defensive runtime checks for things the type system already guarantees Tests for framework behavior rather than business logic Excessive error handling that obscures the actual code Why Not Negative Instructions? Adding "don't test type systems" or "don't add unnecessary checks" to the Implementer prompt has downstream effects: The model becomes hesitant about ALL testing It skips legitimate edge case tests Quality degrades unpredictably The Solution: Separate Pass Instead of constraining the Implementer, let it be thorough. Then add a focused cleanup agent: # Step 1: Implement (let it be thorough) claude -p "Implement the feature with full TDD. Be thorough with tests." # Step 2: De-sloppify (separate context, focused cleanup) claude -p "Review all changes in the working tree. Remove: - Tests that verify language/framework behavior rather than business logic - Redundant type checks that the type system already enforces - Over-defensive error handling for impossible states - Console.log statements - Commented-out code Keep all business logic tests. Run the test suite after cleanup to ensure nothing breaks." In a Loop Context for feature in " ${features[@]} " ; do # Implement claude -p "Implement $feature with TDD." # De-sloppify claude -p "Cleanup pass: review changes, remove test/code slop, run tests." # Verify claude -p "Run build + lint + tests. Fix any failures." # Commit claude -p "Commit with message: feat: add $feature " done Key Insight Rather than adding negative instructions which have downstream quality effects, add a separate de-sloppify pass. Two focused agents outperform one constrained agent. 6. Ralphinho / RFC-Driven DAG Orchestration The most sophisticated pattern. An RFC-driven, multi-agent pipeline that decomposes a spec into a dependency DAG, runs each unit through a tiered quality pipeline, and lands them via an agent-driven merge queue. Created by enitrat (credit: @enitrat). Architecture Overview RFC/PRD Document │ ▼ DECOMPOSITION (AI) Break RFC into work units with dependency DAG │ ▼ ┌──────────────────────────────────────────────────────┐ │ RALPH LOOP (up to 3 passes) │ │ │ │ For each DAG layer (sequential, by dependency): │ │ │ │ ┌── Quality Pipelines (parallel per unit) ───────┐ │ │ │ Each unit in its own worktree: │ │ │ │ Research → Plan → Implement → Test → Review │ │ │ │ (depth varies by complexity tier) │ │ │ └────────────────────────────────────────────────┘ │ │ │ │ ┌── Merge Queue ─────────────────────────────────┐ │ │ │ Rebase onto main → Run tests → Land or evict │ │ │ │ Evicted units re-enter with conflict context │ │ │ └────────────────────────────────────────────────┘ │ │ │ └──────────────────────────────────────────────────────┘ RFC Decomposition AI reads the RFC and produces work units: interface WorkUnit { id : string ; // kebab-case identifier name : string ; // Human-readable name rfcSections : string []; // Which RFC sections this addresses description : string ; // Detailed description deps : string []; // Dependencies (other unit IDs) acceptance : string []; // Concrete acceptance criteria tier : "trivial" | "small" | "medium" | "large" ; } Decomposition Rules: Prefer fewer, cohesive units (minimize merge risk) Minimize cross-unit file overlap (avoid conflicts) Keep tests WITH implementation (never separate "implement X" + "test X") Dependencies only where real code dependency exists The dependency DAG determines execution order: Layer 0: [unit-a, unit-b] ← no deps, run in parallel Layer 1: [unit-c] ← depends on unit-a Layer 2: [unit-d, unit-e] ← depend on unit-c Complexity Tiers Different tiers get different pipeline depths: Tier Pipeline Stages trivial implement → test small implement → test → code-review medium research → plan → implement → test → PRD-review + code-review → review-fix