What is autonomous-feature?

An end-to-end workflow for turning a high-level software feature idea into a planned, implemented, tested, and reviewed repository change.

It helps develop delegated features, inspect the repository and its tests, create an implementation plan, coordinate Claude's coding and verification work, review findings, and fix valid issues.

It coordinates the work across planning, coding, verification, review, and issue triage while setting boundaries around data, permissions, deployments, and unrelated changes.

autonomous-feature — Skill by quaat

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/quaat/autonomous-development/autonomous-feature

Any agent

npx skills add quaat/autonomous-development --skill autonomous-feature

Clone the repo

git clone --depth 1 https://github.com/quaat/autonomous-development

Made for: Claude Code, Codex.

Or install autonomous-development, the plugin that ships this one along with the rest of its 9 skills, 3 agents.

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 891 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00064	$0.00891
Opus 5	$0.00032	$0.00445
Sonnet 5	$0.00013	$0.00178
Haiku 4.5	$0.00006	$0.00089

Measured 2d ago against content hash a5dca6ce73b1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

autonomous-feature scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/autonomous-feature/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Autonomous feature development

Implement this feature idea:

$ARGUMENTS

Use ultrathink for architecture, compatibility, and review triage. Operate as a state-machine driver: ask the controller what to do next, execute that phase, repeat. Detailed per-phase guidance lives in references/ and is loaded only when a phase needs it.

Non-negotiable boundaries

Preserve unrelated user changes.
Never push, merge, publish, deploy, rotate credentials, or modify remote infrastructure.
Never use danger-full-access, --yolo, bypassPermissions, or equivalent unrestricted modes.
Never apply an irreversible database migration or delete user data.
Do not weaken authorization, validation, tests, or static checks to make the workflow pass.
Codex planning and review executions must remain read-only.
Use no more than the configured review-round budget.
Treat every Codex finding as a proposal requiring evidence-based triage.
Do not create commits unless the user explicitly requested them.

Driver loop

Confirm this is a Git repository. Inspect CLAUDE.md, repository instructions, architecture, status, and tests. Use EnterWorktree for an isolated worktree whenever available — mandatory when the starting worktree has uncommitted changes.
Initialize:
```
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/controller.py" doctor
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/controller.py" init --feature "$ARGUMENTS" --mode auto
```
init prints the run-state.json path and run ID. With multiple concurrent runs, pass --run-id <run-id> to all subsequent commands. If doctor reports a missing prerequisite, mark the run blocked and report it rather than bypassing it.
Repeatedly ask the controller for the next phase, then execute it:
```
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/controller.py" next-action --json
```
The response gives phase, required_action, completion_condition, and references. Read the referenced file under references/ for that phase and follow it until the completion condition holds. Phase references:

Read the full file on GitHub · 102 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 102 lines · 64 tokens per session scan A a5dca6ce73b1

Subscribe to this mod's changes

autonomous-feature is a skill published in the GitHub repository quaat/autonomous-development (2 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 891 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Code review Planning

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

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brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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auto-perf-optimize

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chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

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cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

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