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.

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 GitHub Copilot autonomously in loops. Covers everything from simple copilot-cli 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 (copilot-cli)

The simplest loop. Break daily development into a sequence of non-interactive copilot-cli 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 copilot-cli flag runs GitHub Copilot 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
copilot-cli "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)
copilot-cli "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
copilot-cli "Run the full build, lint, type check, and test suite. Fix any failures. Do not add new features."

# Step 4: Commit
copilot-cli "Create a conventional commit for all staged changes. Use 'feat: add OAuth2 login flow' as the message."

Key Design Principles

1. Each step is isolated — A fresh context window per copilot-cli call means no context bleed between steps.
2. Order matters — Steps execute sequentially. Each builds on the filesystem state left by the previous.
3. Negative instructions are dangerous — Don't say "don't test type systems." Instead, add a separate cleanup step (see De-Sloppify Pattern).
4. Exit codes propagate — set -e stops the pipeline on failure.

Variations

With model routing:

# Research with Opus (deep reasoning)
copilot-cli --model opus "Analyze the codebase architecture and write a plan for adding caching..."

# Implement with Sonnet (fast, capable)
copilot-cli "Implement the caching layer according to the plan in docs/caching-plan.md..."

# Review with Opus (thorough)
copilot-cli --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
copilot-cli "Read .claude-context.md for priorities. Work through them in order."
rm .claude-context.md

With --allowedTools restrictions:

# Read-only analysis pass
copilot-cli --allowedTools "Read,Grep,Glob" "Audit this codebase for security vulnerabilities..."

# Write-only implementation pass
copilot-cli --allowedTools "Read,Write,Edit,Bash" "Implement the fixes from security-audit.md..."

---

2. NanoClaw REPL

EGP's built-in persistent loop. A session-aware REPL that calls copilot-cli 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

1. Loads conversation history from ~/.github/claw/{session}.md
2. Each user message is sent to copilot-cli with full history as context
3. Responses are appended to the session file (Markdown-as-database)
4. 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

1. Spec Analysis — Orchestrator reads a specification file (Markdown) defining what to generate
2. Directory Recon — Scans existing output to find the highest iteration number
3. 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)
4. Wave Management — For infinite mode, deploys waves of 3-5 agents until context is exhausted

Implementation via GitHub Copilot Commands

Create .github/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 GitHub Copilot 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 copilot-cli with enhanced prompt              │
│  3. (Optional) Reviewer pass — separate copilot-cli   │
│  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 (copilot-cli)         │
│  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 copilot-cli invocations.

CI Failure Recovery

When PR checks fail, Continuous Claude automatically:

1. Fetches the failed run ID via gh run list
2. Spawns a new copilot-cli with CI fix context
3. Claude inspects logs via gh run view, fixes code, commits, pushes
4. 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)
copilot-cli "Implement the feature with full TDD. Be thorough with tests."

# Step 2: De-sloppify (separate context, focused cleanup)
copilot-cli "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
  copilot-cli "Implement $feature with TDD."

  # De-sloppify
  copilot-cli "Cleanup pass: review changes, remove test/code slop, run tests."

  # Verify
  copilot-cli "Run build + lint + tests. Fix any failures."

  # Commit
  copilot-cli "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
large	research → plan → implement → test → PRD-review + code-review → review-fix → final-review

This prevents expensive operations on simple changes while ensuring architectural changes get thorough scrutiny.

Separate Context Windows (Author-Bias Elimination)

Each stage runs in its own agent process with its own context window:

Stage	Model	Purpose
Research	Sonnet	Read codebase + RFC, produce context doc
Plan	Opus	Design implementation steps
Implement	Codex	Write code following the plan
Test	Sonnet	Run build + test suite
PRD Review	Sonnet	Spec compliance check
Code Review	Opus	Quality + security check
Review Fix	Codex	Address review issues
Final Review	Opus	Quality gate (large tier only)

Critical design: The reviewer never wrote the code it reviews. This eliminates author bias — the most common source of missed issues in self-review.

Merge Queue with Eviction

After quality pipelines complete, units enter the merge queue:

Unit branch
    │
    ├─ Rebase onto main
    │   └─ Conflict? → EVICT (capture conflict context)
    │
    ├─ Run build + tests
    │   └─ Fail? → EVICT (capture test output)
    │
    └─ Pass → Fast-forward main, push, delete branch

File Overlap Intelligence:

• Non-overlapping units land speculatively in parallel
• Overlapping units land one-by-one, rebasing each time

Eviction Recovery: When evicted, full context is captured (conflicting files, diffs, test output) and fed back to the implementer on the next Ralph pass:

## MERGE CONFLICT — RESOLVE BEFORE NEXT LANDING

Your previous implementation conflicted with another unit that landed first.
Restructure your changes to avoid the conflicting files/lines below.

{full eviction context with diffs}

Data Flow Between Stages

research.contextFilePath ──────────────────→ plan
plan.implementationSteps ──────────────────→ implement
implement.{filesCreated, whatWasDone} ─────→ test, reviews
test.failingSummary ───────────────────────→ reviews, implement (next pass)
reviews.{feedback, issues} ────────────────→ review-fix → implement (next pass)
final-review.reasoning ────────────────────→ implement (next pass)
evictionContext ───────────────────────────→ implement (after merge conflict)

Worktree Isolation

Every unit runs in an isolated worktree (uses jj/Jujutsu, not git):

/tmp/workflow-wt-{unit-id}/

Pipeline stages for the same unit share a worktree, preserving state (context files, plan files, code changes) across research → plan → implement → test → review.

Key Design Principles

1. Deterministic execution — Upfront decomposition locks in parallelism and ordering
2. Human review at leverage points — The work plan is the single highest-leverage intervention point
3. Separate concerns — Each stage in a separate context window with a separate agent
4. Conflict recovery with context — Full eviction context enables intelligent re-runs, not blind retries
5. Tier-driven depth — Trivial changes skip research/review; large changes get maximum scrutiny
6. Resumable workflows — Full state persisted to SQLite; resume from any point

When to Use Ralphinho vs Simpler Patterns

Signal	Use Ralphinho	Use Simpler Pattern
Multiple interdependent work units	Yes	No
Need parallel implementation	Yes	No
Merge conflicts likely	Yes	No (sequential is fine)
Single-file change	No	Yes (sequential pipeline)
Multi-day project	Yes	Maybe (continuous-claude)
Spec/RFC already written	Yes	Maybe
Quick iteration on one thing	No	Yes (NanoClaw or pipeline)

---

Choosing the Right Pattern

Decision Matrix

Is the task a single focused change?
├─ Yes → Sequential Pipeline or NanoClaw
└─ No → Is there a written spec/RFC?
         ├─ Yes → Do you need parallel implementation?
         │        ├─ Yes → Ralphinho (DAG orchestration)
         │        └─ No → Continuous Claude (iterative PR loop)
         └─ No → Do you need many variations of the same thing?
                  ├─ Yes → Infinite Agentic Loop (spec-driven generation)
                  └─ No → Sequential Pipeline with de-sloppify

Combining Patterns

These patterns compose well:

1. Sequential Pipeline + De-Sloppify — The most common combination. Every implement step gets a cleanup pass.

2. Continuous Claude + De-Sloppify — Add --review-prompt with a de-sloppify directive to each iteration.

3. Any loop + Verification — Use EGP's /verify command or verification-loop skill as a gate before commits.

4. Ralphinho's tiered approach in simpler loops — Even in a sequential pipeline, you can route simple tasks to Haiku and complex tasks to Opus:

   # Simple formatting fix
   copilot-cli --model haiku "Fix the import ordering in src/utils.ts"
   
   # Complex architectural change
   copilot-cli --model opus "Refactor the auth module to use the strategy pattern"
   

---

Anti-Patterns

Common Mistakes

1. Infinite loops without exit conditions — Always have a max-runs, max-cost, max-duration, or completion signal.

2. No context bridge between iterations — Each copilot-cli call starts fresh. Use SHARED_TASK_NOTES.md or filesystem state to bridge context.

3. Retrying the same failure — If an iteration fails, don't just retry. Capture the error context and feed it to the next attempt.

4. Negative instructions instead of cleanup passes — Don't say "don't do X." Add a separate pass that removes X.

5. All agents in one context window — For complex workflows, separate concerns into different agent processes. The reviewer should never be the author.

6. Ignoring file overlap in parallel work — If two parallel agents might edit the same file, you need a merge strategy (sequential landing, rebase, or conflict resolution).

---

References

Project	Author	Link
Ralphinho	enitrat	credit: @enitrat
Infinite Agentic Loop	disler	credit: @disler
Continuous Claude	AnandChowdhary	credit: @AnandChowdhary
NanoClaw	EGP	/claw command in this repo
Verification Loop	EGP	skills/verification-loop/ in this repo