autonomous-loops
Autonomous agent loop patterns: sequential pipelines, persistent REPL sessions, parallel spec-driven generation, PR automation, cleanup passes, and RFC-driven DAG orchestration. Ch…
Deonte Cooper
@djc00p
What This Skill Does
Provides six autonomous loop patterns for Claude Code: sequential pipelines, persistent REPL sessions, parallel spec-driven generation, PR automation, cleanup passes, and RFC-driven DAG orchestration. Each pattern is selected by complexity and includes exit conditions, context bridging, and isolation principles.
Replaces manually running Claude Code for each step by enabling spec-driven development, CI/CD-style pipelines, and iterative refinement without human intervention between iterations.
When to Use It
- Chain multiple Claude Code calls into a linear workflow for daily tasks
- Run a persistent interactive Claude session with history for exploratory development
- Deploy multiple agents in parallel from a shared spec to generate content or variations
- Automate a PR creation, CI fix, and auto-merge cycle for iterative multi-day projects
- Add a quality cleanup pass after any implementation to reduce sloppiness
- Orchestrate a multi-unit feature with a dependency graph and merge queue using RFCs
Install
$ openclaw skills install @djc00p/autonomous-loopsAutonomous Loops — Patterns for Agent Automation
⚠️ Security & Consent — READ FIRST
This skill teaches unattended repository automation. It can create branches, open pull requests, retry CI failures, and merge code without a human in the loop. That power is dangerous in the wrong hands and on the wrong target.
Before using any pattern in this skill, you MUST:
- Get explicit, informed consent from the repository owner. "I want to automate PRs" is not consent for "merge my production code at 3 AM without review."
- Default to dry-run. All patterns below start in
--disable-commits/ no-write mode. Only flip the switch when you understand what each flag does.- Use protected branches + least-privileged credentials. The GitHub token this loop uses must not have admin/org-owner scopes. A scoped fine-grained PAT scoped to one repo is the minimum.
- Set hard limits. Every loop must have
--max-runs,--max-cost, and--max-duration. Without these, a runaway loop will burn budget or destroy state.- Never loop on production. Run on a fork, a scratch repo, or a clearly-labeled branch. The main branch should be merged by a human.
- Audit your inputs. Specs, directory listings, and captured diffs may contain secrets, customer data, internal paths, or proprietary code. Never pipe those into an external agent command without scrubbing them first.
Patterns in this skill that touch the network, filesystem, or git remotes will refuse to run unless the corresponding safety flags are explicitly set. If a pattern silently merges or pushes, that is a bug — file an issue.
See
references/security-checklist.mdfor the full audit before first use.
Running agents in loops enables spec-driven development, CI/CD-style pipelines, and iterative refinement without human intervention between steps. Used correctly, this is a productivity multiplier. Used carelessly, it can drain a bank account, leak secrets, or rewrite a production codebase.
Quick Start
Choose your pattern by complexity:
- Sequential Pipeline (simple) — Chain
claude -pcalls for linear workflows - Persistent REPL (simple) — Interactive sessions with history
- Spec-Driven Parallel (medium) — Deploy N agents from spec, manage waves
- PR Automation Loop (medium) — PR creation, CI fix, auto-merge
- De-Sloppify Pass (add-on) — Cleanup step after any implementation
- RFC-Driven DAG (advanced) — Multi-unit parallel work with dependency graph
Pattern Spectrum
| Pattern | Setup | Complexity | Default Mode | Best For |
|---|---|---|---|---|
| Sequential Pipeline | Bash script | Low | Dry-run | Daily tasks, scripted workflows |
| REPL | Node/CLI | Low | Read-only | Interactive development |
| Parallel Agents | Agent loop | Medium | Dry-run | Content generation, spec variations |
| PR Loop | Shell script | Medium | --disable-commits | Iterative multi-day projects |
| De-Sloppify | Add-on to any | Optional | Dry-run | Quality cleanup after implementation |
| DAG Orchestration | Python/Node | High | Dry-run | Large features, parallel units, merge coordination |
References
references/security-checklist.md— Read before any first usereferences/sequential-pipeline.md— Basicclaude -ploops with examplesreferences/persistent-repl.md— Session persistence + secrets-handling rulesreferences/parallel-agents.md— Spec-driven deployment with worktree isolationreferences/pr-automation.md— Continuous PR loop with consent gates and CI gatesreferences/de-sloppify.md— Quality cleanup patternreferences/dag-orchestration.md— RFC-driven multi-unit coordination + merge-conflict recovery
Key Principles
- Isolation — Each loop iteration gets fresh context (no bleed-through)
- Context Persistence — Use files (SHARED_TASK_NOTES.md) to bridge iterations
- Exit Conditions — Always set max-runs, max-cost, max-duration, or completion signal
- No Blind Retries — Capture error context for next iteration (and redact secrets first)
- Separate Concerns — Different loop patterns for different problem sizes
- Default Deny — Network writes, git pushes, and merges require explicit opt-in flags
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 ❌ Auto-merge without explicit human approval on every merge ❌ Skipping the security checklist before first use
Adapted from everything-claude-code by @affaan-m (MIT)
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