affaan-m/ecc1 file

Autonomous Loops

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

Specification
Skill ID
affaan-m/ecc/autonomous-loops
Publisher
affaan-m
Repository
ecc
Installs
0
Files
1
Synced
Sep 23, 2026
How to use it

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affaan-m/ecc/autonomous-loopsInstalls these files
  • SKILL.md

What this skill tells the agent

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:

PatternComplexityBest For
Sequential PipelineLowDaily dev steps, scripted workflows
NanoClaw REPLLowInteractive persistent sessions
Infinite Agentic LoopMediumParallel content generation, spec-driven work
Continuous Claude PR LoopMediumMulti-day iterative projects with CI gates
De-Sloppify PatternAdd-onQuality cleanup after any Implementer step
Ralphinho / RFC-Driven DAGHighLarge 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

  1. Each step is isolated — A fresh context window per claude -p 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)
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

  1. Loads conversation history from ~/.claude/claw/{session}.md
  2. Each user message is sent to claude -p 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 CaseNanoClawSequential Pipeline
Interactive explorationYesNo
Scripted automationNoYes
Session persistenceBuilt-inManual
Context accumulationGrows per turnFresh each step
CI/CD integrationPoorExcellent

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)
  1. 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

CountStrategy
1-5All agents simultaneously
6-20Batches of 5
infiniteWaves of 3-5, progressive sophistication

Key Insight: Uniqueness via Assignment

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