Autoprompt Skill
A multi-agent skill for coding agents that layers coordination, execution, and independent review to cut failures by 45% on agentic coding tasks.
- Source repo
- Spielewoy/autoprompt-skill
- Stars
- ★ 1.2k
- Last updated
- 19d ago
- License
- MIT
- Primary language
- JavaScript
- FA score
- 69/100 · Some gaps
At a glance
- How it runs
- Works with
- Portable with changesCodex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A developer on Claude Code or OpenCode whose agent keeps producing broken code, using Autoprompt to reduce failure rate.
- Not a fit if
- Teams without a local terminal runtime (Node.js 20+, Python 3.11+, Bash 4.3+)
- Users doing very small tasks who won't accept ~3x time and 2x tokens
- Teams needing custom model routing on all hosts — only Claude Code and Codex support it
- Source review
- 69/100 · Some gaps
What does this agent do, and when should you use it?
Autoprompt is an MIT-licensed coding-agent skill (v1.0.4) distributed via npm from the Spielewoy/autoprompt-skill repository. It organizes work through a subagent hierarchy — coordinators, a manager, execution lanes, and independent checks — so no single agent plans, approves, and verifies its own output. It installs into nine supported coding agents: Claude Code, Codex, OpenCode, Kilo Code, VS Code, Prime Agent, Oh My Pi, DeepSeek Harness, and Reasonix. In a measured Terminal-Bench 2.1 run with OpenCode 1.18.7, solves went from 60/89 to 73/89 and failures dropped from 29 to 16 — a 45% reduction and +14.61 points. It runs locally and requires Node.js 20+, Python 3.11+, and Bash 4.3+, with run controls like mode=, max_subs=, and agents= for concurrency and model routing. The README is explicit about the trade-off: roughly 3x the time and 2x the tokens, estimates based on user reports rather than retained measurement logs.
You invoke it with /autoprompt <goal> ($autoprompt in Codex, /skill:autoprompt in Oh My Pi), optionally with concurrency controls: mode=tokensaver caps active subagents at six, mode=wide opens every ready lane, and mode=custom max_subs=N sets your own ceiling. The skill takes the goal through a loop of planning, build, review, tests, sign-off, and sweep: coordinator layers decompose the task, execution lanes implement it, and independent check layers verify results. Where the host supports it, agents= routes subagents to specific models; otherwise the active or parent model is inherited. Documented example goals include fixing a race condition with a regression test, building a booking flow end to end, comparing job-queue libraries against a codebase, and migrating models with limited parallelism. After installation, autoprompt doctor --strict checks all detected installations and autoprompt uninstall [PROVIDER] removes them.
- A developer on Claude Code or OpenCode whose agent keeps producing broken code, using Autoprompt to reduce failure rate.
- A team migrating many models or files, limiting parallel work with
mode=custom max_subs=4to avoid machine overload. - An engineer who wants a regression test written alongside every bug fix.
- A Codex user building a multi-step feature (e.g., a booking flow from API to checkout) with
mode=wideto open all execution lanes. - A developer comparing libraries or architectures who wants a researched recommendation grounded in their codebase.
How do you install or deploy this agent?
Prerequisites: Node.js 20+, Python 3.11+ exposed as python with PyYAML, Bash 4.3+ on macOS/Linux; Git only for the source install. Install with npm install -g autoprompt-skill, then run autoprompt to launch the interactive installer, choose your coding agent, and confirm its path (enter N to supply a different path). From source: git clone https://github.com/Spielewoy/autoprompt-skill && cd autoprompt-skill && npm install -g . && autoprompt. Verify with autoprompt doctor --strict.
How do you use this agent?
Invoke explicitly inside a supported agent: /autoprompt <goal> in Claude Code/OpenCode, $autoprompt <goal> in Codex, /skill:autoprompt in Oh My Pi. Examples: /autoprompt fix the registration race and add a regression test; /autoprompt mode=wide build the booking flow from API to checkout; /autoprompt mode=custom max_subs=4 migrate every model. It does not start in the background; you trigger it each time.
What are this agent's strengths and limitations?
- Measured benchmark evidence: OpenCode 1.18.7 failures on Terminal-Bench 2.1 dropped from 29 to 16 (45% fewer), +14.61 points.
- Nine supported coding agents (Claude Code, Codex, OpenCode, Kilo, VS Code, Prime Agent, Oh My Pi, DeepSeek Harness, Reasonix), each with audited versions documented.
- Layered design separates coordination, management, execution, and independent judgment, preventing self-approval and self-verification by one agent.
- Install lifecycle is verifiable via
autoprompt doctor, update/repair flows, and per-provider uninstall.
- Significant cost: the README states roughly 3x the time and 2x the tokens, based on user experience reports, not measured logs.
- For very small tasks, the trade-off may differ heavily.
- Requires a local runtime with Node.js 20+, Python 3.11+, and Bash 4.3+ — not usable directly in purely cloud or GUI-only environments.
- Custom
agents=model routing works only on Claude Code and Codex; the other seven hosts inherit the current or parent model.
How does this agent compare with similar options?
The README cites DeepSeek's own 82.7% Terminal-Bench 2.1 result as a reference point but explicitly notes it used DeepSeek's own test setup and is not a comparable third run.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Autoprompt Skill This agent | 69 · Some gaps | CLIFree + model costs | ★ 1.2k | 19d ago | JavaScript | Codex · Claude Code |
| Smart Ralph | 73 · Some gaps | Agent plugin / skillFree + model costs | ★ 561 | 1mo ago | Shell | Codex · Claude Code |
| AI-DLC Workflows | 49 · Major gaps | CLIFree + model costs | ★ 5.5k | today | TypeScript | Codex · Claude Code |
| ECC Engineering Harness | 66 · Some gaps | Agent plugin / skillFreemium | ★ 286k | 2d ago | JavaScript | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Adapter tests show allowlisted child dispatch, depth/parent-edge validation, mission-binding hash revalidation failing closed, explicit `/autoprompt` triggering, and interactive install/uninstall/doctor paths; CI uses SHA-pinned actions with minimal permissions. Deductions: the installer rewrites local agent configs but the source shown offers no per-change manifest or explicit user confirmation of exact writes; sensitive-data handling (telemetry, logs, tokens) is entirely unaddressed in the provided files.
package. declares zero npm runtime dependencies; requirements are explicitly listed (Node 20+, Python 3.11+, Bash 4.3+, PyYAML); CI runs across Node 20/22/24 and Windows with an extensive contract and lifecycle test suite. Deductions: user-facing failure behavior is only inferable from test fixtures (probe timeouts, binding denials); the concrete semantics of `doctor --strict` and error-message quality are not documented.
A per-provider support table lists nine agents with audited versions, plus a custom-agent compatibility guide and custom-model guide; capability boundaries for mode=/max_subs=/agents= are marked per provider, with distinct trigger syntax for Codex and Oh My Pi. Deductions: audience is developers already on these hosts; unsuitable scenarios are only footnoted ('may differ heavily' for small tasks) rather than systematically documented.
Documentation architecture is strong: contents, install, benchmarks, controls, workflow, examples, FAQ, and license are all present with FAQ answers split into dedicated files; MIT license text is in-repo and install/update/uninstall/check commands are complete. Deductions: no CHANGELOG exists, so the 1.0.4 version has no traceable history; CONTRIBUTING/SECURITY/SUPPORT are link-only and maintainer/update commitments cannot be verified from the provided files.
The 45% failure reduction (29→16) maps to one measured OpenCode run, and the ~3x time / ~2x token trade-off is honestly labeled a planning estimate, not a measurement; DeepSeek's score is explicitly flagged as non-comparable — a solid cost-benefit disclosure. Deductions: the benefit rests on a single run of a single provider; timing/token logs were not retained, and actual output usability cannot be confirmed statically.
A dedicated benchmark methodology/'evidence boundaries' doc is linked, and measured versus estimated figures are clearly separated (earning full marks for fact/inference separation); a benchmark-request template provides a traceability path. Deductions: everything is single-source self-attestation — no third-party reproduction or independent corroboration, and the benchmark doc itself is not among the provided files, so the '45%' figure can only be taken on the README's word.
- The installer writes into local coding-agent configurations and skill directories; inspect the writes in an isolated environment first and verify that `autoprompt uninstall` fully rolls back.
- The 45% failure reduction comes from a single measured OpenCode run; the ~3x time / ~2x token cost is an estimate and may not pay off on small tasks — validate on your own workload at small scale.
- The publisher is unverified and identity is unknown; audit the installer scripts in a controlled environment before adopting in production projects.
- Sensitive-data handling (logs, tokens, telemetry) is not mentioned anywhere in the provided files; organizations handling private code should assess data flows independently.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v2.1.0 v2.1
Release notes excerpt
Native Windows runtime support without WSL2 or a VM. - Fix OpenCode npm executable shims and PowerShell argument forwarding (#28). - Add native Windows admission, including Claude Code 2.1.270 (#27). - Complete platform fixes and x64/ARM64 verification across Windows, macOS, and Linux. - Isolate Windows Codex credentials and enforce activation-bound network and process cleanup. Windows Codex requires an administrator-started controller and an initialized official sandbox. Codex checks cover activation, sandbox enforcement, and cleanup; live-model missions were not tested. …View on GitHub ↗ -
v2.0.0 v2.0
Release notes excerpt
Autoprompt v2 brings the Codex v2 workflow to all 11 supported providers, including Grok Build and Hermes, with model and effort controls, bounded subagents, and installer lifecycle support. Download the package or your platform’s installer kit below. The standalone installers fetch this exact v2 release. Installation instructions and diagrams are updated in English, Chinese, Korean, Spanish, and Arabic. …View on GitHub ↗ -
v1.0.4 Autoprompt Skill 1.0.4
Release notes excerpt
Autoprompt Skill 1.0.4 Autoprompt turns one explicit goal into a closed plan, build, test, review, repair, and verification loop. Highlights - 45% fewer failures in the published Terminal-Bench 2.1 run: 29 failures fell to 16 - State-aware CLI scans nine coding agents, reports installed versions, and offers install, update, repair, doctor, and uninstall flows - Older Codex installs are detected and updated in place - Explicit invocation keeps the orchestration loop isolated from ordinary coding requests …View on GitHub ↗
FAQ
Does Autoprompt mean I literally do not have to prompt?
How autonomous is it?
What is the actual overhead?
Is my coding agent supported?
Why doesn't it start in the background?
/autoprompt <goal>.