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djc00p

autonomous-loops

by Deonte Cooper · GitHub ↗ · v1.0.1 · MIT-0
linuxdarwinwin32 ✓ Security Clean
127
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Install in OpenClaw
/install autonomous-loops
Description
Autonomous Claude Code loop patterns: sequential pipelines, persistent REPL sessions, parallel spec-driven generation, PR automation, cleanup passes, and RFC...
README (SKILL.md)

Autonomous Loops — Patterns for Claude Code Automation

Running Claude in loops enables spec-driven development, CI/CD-style pipelines, and iterative refinement without human intervention between steps.

Quick Start

Choose your pattern by complexity:

  1. Sequential Pipeline (simple) — Chain claude -p calls for linear workflows
  2. Persistent REPL (simple) — Interactive sessions with history
  3. Spec-Driven Parallel (medium) — Deploy N agents from spec, manage waves
  4. PR Automation Loop (medium) — PR creation, CI fix, auto-merge
  5. De-Sloppify Pass (add-on) — Cleanup step after any implementation
  6. RFC-Driven DAG (advanced) — Multi-unit parallel work with dependency graph

Pattern Spectrum

Pattern Setup Complexity Best For
Sequential Pipeline Bash script Low Daily tasks, scripted workflows
REPL Node/CLI Low Interactive development
Parallel Agents Claude Code loop Medium Content generation, spec variations
PR Loop Shell script Medium Iterative multi-day projects
De-Sloppify Add-on to any Optional Quality cleanup after implementation
DAG Orchestration Python/Node High Large features, parallel units, merge coordination

References

  • references/sequential-pipeline.md — Basic claude -p loops with examples
  • references/persistent-repl.md — NanoClaw-style session persistence
  • references/parallel-agents.md — Spec-driven deployment with wave management
  • references/pr-automation.md — Continuous Claude PR loop with CI gates
  • references/de-sloppify.md — Quality cleanup pattern
  • references/dag-orchestration.md — RFC-driven multi-unit coordination

Key Principles

  1. Isolation — Each loop iteration gets fresh context (no bleed-through)
  2. Context Persistence — Use files (SHARED_TASK_NOTES.md) to bridge iterations
  3. Exit Conditions — Always set max-runs, max-cost, max-duration, or completion signal
  4. No Blind Retries — Capture error context for next iteration
  5. Separate Concerns — Different loop patterns for different problem sizes

Decision Matrix

Is this a single focused change?
├─ Yes → Sequential Pipeline
└─ No → Do you have a spec/RFC?
         ├─ Yes → Do you need parallel work?
         │        ├─ Yes → DAG Orchestration
         │        └─ No → PR Automation Loop
         └─ No → Do you need many variations?
                  ├─ Yes → Parallel Agents + Spec
                  └─ No → Sequential Pipeline + De-Sloppify

Anti-Patterns

❌ Infinite loops without exit conditions ❌ No context bridge between iterations ❌ Retrying the same failure without capturing error context ❌ Negative instructions instead of cleanup passes ❌ All agents in one context window (reviewer should never be the author) ❌ Ignoring file overlap in parallel work


Adapted from everything-claude-code by @affaan-m (MIT)

Usage Guidance
This skill is coherent with its stated goal (automating Claude-driven pipelines), but it enables high-impact actions: creating branches, committing, pushing, opening and merging PRs, and auto-fixing CI failures. Before using it: 1) test in a sandbox repository and enable --disable-commits or dry-run modes; 2) set conservative caps (max-runs, max-cost, max-duration) and require manual approval before merges; 3) ensure your gh CLI is scoped to only the repos you intend (use a token with minimal scopes or a machine/service account); 4) review and control any session files (~/.claude/sessions) for sensitive data before allowing automation to read them; 5) prefer human review gates for critical repos; and 6) audit any scripts you run that they don't pipe remote content directly to bash. If you want tighter safety, require human-in-the-loop checks before merge or disable autonomous invocation for this skill.
Capability Analysis
Type: OpenClaw Skill Name: autonomous-loops Version: 1.0.1 The skill bundle provides a comprehensive set of architectural patterns and documentation for automating Claude Code workflows, including sequential pipelines, parallel agent execution, and PR automation. The content consists of instructional markdown files and bash script examples that align with the stated purpose of enhancing autonomous development loops. There is no evidence of malicious intent, data exfiltration, or unauthorized execution; in fact, the documentation includes security-conscious advice such as warning against piping scripts directly to bash in references/pr-automation.md.
Capability Tags
cryptorequires-oauth-token
Capability Assessment
Purpose & Capability
The name/description (autonomous loops, pipelines, PR automation, DAG orchestration) aligns with the declared requirements: gh/git for repository and PR operations, node for the persistent REPL script examples, and CLAW_SESSION/CLAW_SKILLS to select session context/skill sets. Nothing requested appears unrelated to the described automation patterns.
Instruction Scope
SKILL.md instructs the agent to read/write session files (~/.claude/sessions/{name}.md), create/update SHARED_TASK_NOTES.md, run claude -p, run git/gh commands (create branches, push, create and merge PRs), and poll CI logs. These actions are within the skill's scope but are powerful: they will read local repo files, modify code, commit/push, and can auto-fix and auto-merge PRs if enabled. The doc correctly advises limits, but users should be aware these instructions grant the agent effective control over repo changes when invoked.
Install Mechanism
No install spec and no code files are present; this is instruction-only. That lowers supply-chain risk because nothing is downloaded or written by an installer. The skill relies on existing binaries on PATH (gh, git, node).
Credentials
Only CLAW_SESSION and CLAW_SKILLS are required (plus gh/git/node binaries). Those env vars map to session selection and skill composition and make sense for persistent REPL and pattern selection. The skill does not request unrelated credentials (no cloud keys, tokens, or passwords). Note: using gh implies the user will have gh auth configured — that grants repository privileges, so treat gh credentials carefully.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request permanent forced inclusion. The instructions describe persisting session and task files and automating PR lifecycle, which is expected behavior for this class of skill. Because autonomous invocation is allowed, the usual caution applies: if the agent is allowed to run these loops unattended, it may create/merge PRs and spend tokens/costs unless caps are set.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install autonomous-loops
  3. After installation, invoke the skill by name or use /autonomous-loops
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
Fixed metadata: added required bins (gh, git, node) and env vars (CLAW_SESSION, CLAW_SKILLS) to requires
v1.0.0
Initial release. Autonomous agent loop patterns with quality gates and context management. Adapted from everything-claude-code by @affaan-m (MIT)
Metadata
Slug autonomous-loops
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is autonomous-loops?

Autonomous Claude Code loop patterns: sequential pipelines, persistent REPL sessions, parallel spec-driven generation, PR automation, cleanup passes, and RFC... It is an AI Agent Skill for Claude Code / OpenClaw, with 127 downloads so far.

How do I install autonomous-loops?

Run "/install autonomous-loops" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is autonomous-loops free?

Yes, autonomous-loops is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does autonomous-loops support?

autonomous-loops is cross-platform and runs anywhere OpenClaw / Claude Code is available (linux, darwin, win32).

Who created autonomous-loops?

It is built and maintained by Deonte Cooper (@djc00p); the current version is v1.0.1.

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