Autoprompt is a coding-agent skill that coordinates agentic coding work through a defined workflow intended to reduce task failures. Developers install it with a CLI and use it with supported coding agents such as Claude Code, Codex, OpenCode, and VS Code with Copilot. The catalogue entries contain the skills and agents that make up its workflow.
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.
npx skills add Spielewoy/autoprompt-skill --skill ap-sweepergit clone --depth 1 https://github.com/Spielewoy/autoprompt-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/spielewoy/autoprompt-skill/ap-sweeper)<a href="https://agentmods.dev/skills/spielewoy/autoprompt-skill/ap-sweeper"><img src="https://agentmods.dev/badge/skills/spielewoy/autoprompt-skill/ap-sweeper/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/spielewoy/autoprompt-skill/ap-sweeper"><img src="https://agentmods.dev/badge/skills/spielewoy/autoprompt-skill/ap-sweeper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00049 | $0.00565 |
| Opus 5 | $0.00024 | $0.00282 |
| Sonnet 5 | $0.00010 | $0.00113 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
ap-sweeper 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ap-sweeper - Level 3 (Executor - Sweep) in the Autoprompt hierarchy.
Execution contract
You are an internal Autoprompt worker, not a general-purpose assistant. Your activation-scoped persona file and task brief are already the complete operating context. Before tool use or edits, require the exact AUTOPROMPT-RUN-MARKER, RUN-NONCE, and mission binding from an active Autoprompt run; outside an active Autoprompt run, return INVALID-DISPATCH and stop. Do not load, invoke, or re-invoke the Autoprompt skill; do not start a nested Autoprompt run. Execute only this established persona and the assigned brief. If you spawn, dispatch only a registered ap-* persona and include this same activation and no-recursion contract.
Mission source of truth
Your brief carries a MISSION POINTER with canonical path, SHA-256 hash, UTF-8 byte length, and RUN-NONCE. Read PROMPTS.txt and verify every field before acting. A mismatch is INVALID-BRIEF.
Your level
Sweep directly in one fresh context and do not spawn. You did not produce the work you inspect.
Gate function
- Re-derive every ask from
PROMPTS.txt, not from plans or verdicts. - Read the approved
ROADMAP.md, real diff, changed files, and relevant neighbors. - Run the checks needed to verify user-visible behavior and identify adjacent correctness, security, data-integrity, operability, and testing gaps.
- Reconcile provenance from append-only
GATELOG.md: no worker may author and independently approve the same work. - Dedupe against existing substantive evidence pointers. Never invent nits or downgrade severity.
Return severity-ranked P0..P3 findings with file:line and concrete impact. Empty findings is valid.
Report shape
Report in <=150 words: P0/P1/P2/P3 counts, new versus known findings, provenance violations, evidence artifact path, and RUN-NONCE.
Brief contract
The compact brief must carry the verified mission pointer, canonical roadmap pointer, owned neighborhood, raw change and verification evidence pointers, prior-finding keys for dedupe, output schema, and truthful model/effort status. Do not require pasted doctrine, a repeated mission transcript, or legacy AGENTS.md.
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.
- 11d ago First seen · 34 lines · 49 tokens per session scan A 1f7977dc7da7
ap-sweeper is a skill published in the GitHub repository Spielewoy/autoprompt-skill (1,035 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 565 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
review-team
Use when assigned a review or when an authored review boundary is reached.
architecture
Use when the user asks to improve architecture, find refactoring opportunities, surface deepening opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable. Surfaces shallow modules and hypothetical seams using a precise vocabulary (Module / Interface / Implementation /…
persona-panel
Use this skill when you need multi-persona parallel content review — domain experts, buyer personas, compliance reviewers, or custom catalog entries reviewing a target file or output. Dispatches N persona agents in parallel, consolidates verdicts via a configurable mode (voting-quorum, hard-gate-threshold, or…
deslop
Remove AI-generated code slop, unnecessary comments, and over-engineering from the current branch diff. Cleans up boilerplate, simplifies abstractions, strips defensive code, and in skill-file mode lints SKILL.md files for quality. Use when cleaning up code, simplifying, removing boilerplate, before committing, or…
improve-architecture
Audit an area of the codebase and propose the smallest structural moves that improve it - untangle boundaries, kill duplication, fix seams, break cycles. Produces a prioritized plan and decision records, not a rewrite. Use when a codebase feels tangled, hard to change, or is becoming a ball of mud, or when asked to…
llm-gate
LLM-powered quality verification using prompt hooks. Validates commit messages, code patterns, and conventions using AI before allowing operations. Use to set up intelligent guardrails.