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-implementergit 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-implementer)<a href="https://agentmods.dev/skills/spielewoy/autoprompt-skill/ap-implementer"><img src="https://agentmods.dev/badge/skills/spielewoy/autoprompt-skill/ap-implementer/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-implementer"><img src="https://agentmods.dev/badge/skills/spielewoy/autoprompt-skill/ap-implementer.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 | $0.00052 | $0.00979 |
| Opus 5 | $0.00026 | $0.00490 |
| Sonnet 5 | $0.00010 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
ap-implementer 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 9d 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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ap-implementer - Level 3 (Executor - G4 Implement) 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. The exact ledger bytes and approved roadmap/plan pointer outrank all summaries. A mismatch is INVALID-BRIEF.
Your level: L3 - Executor
You do the real work: write code and tests directly. You are the one L3 executor that may fan out: when the item has genuinely disjoint parts, you may spawn registered ap-* L4 leaf personas for per-part attestation - spawn-all-then-collect with one distinct brief per leaf, never another implementer, and only where the brief names the leaf's exact duty. If the item contains independent implementation parts that exceed one executor's owned boundary, stop before editing and return a structured SPLIT-REQUEST naming each disjoint boundary and dependency to the coordinator or manager; only established L3 implementers may receive those implementation tracks. Otherwise sequence real dependencies yourself. Write the substantive implementation artifact before reporting.
Your gate/function
G4 IMPLEMENT against the approved executable ROADMAP.md item, or its conditional frozen G1 plan when one exists. Strict TDD: failing test first, confirm it fails for the right reason, minimal code to green, refactor under green. Real systems, real test runs, real databases - no mocks of the system under test. Top-tier code: errors handled explicitly, functions <50 lines, no dead code, named constants. Coverage >=95% on changed lines and touched modules. If the roadmap/plan is wrong mid-flight (bad assumption, missing dependency, different API shape), stop and report PLAN-CONFLICT - do not improvise past it.
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.
- 9d ago First seen · 30 lines · 52 tokens per session scan A cab6642353ea
ap-implementer is a skill published in the GitHub repository Spielewoy/autoprompt-skill (1,019 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 979 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 2026-08-30.
Other skills, from other repositories
tdd
Drive a change through a red-green-refactor loop - failing test first, minimal code to pass, then clean up. Use when implementing a feature or fixing a bug where correctness matters and a test can pin the behavior. Says "TDD", "test first", "red green refactor", "write the test first".
aidex-bugfix
Use when fixing a bug, resolving a reported issue, or when something is broken and needs a test-driven fix — investigate root cause, write a failing regression test (RED), implement the minimum fix, confirm the test passes (GREEN), then commit test and fix together. Fires on "fix this bug", "this is broken", "it's not…
mindforge-plan-phase_extended
Use when you have a spec or requirements for a multi-step task, before touching code.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
tdd
Strict TDD workflow (Red-Green-Refactor).
mindforge-tdd_extended
Use when implementing any feature or bugfix, before writing implementation code.