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.
git 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/agents/spielewoy/autoprompt-skill/ap-execharness-resolver)<a href="https://agentmods.dev/agents/spielewoy/autoprompt-skill/ap-execharness-resolver"><img src="https://agentmods.dev/badge/agents/spielewoy/autoprompt-skill/ap-execharness-resolver/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/agents/spielewoy/autoprompt-skill/ap-execharness-resolver"><img src="https://agentmods.dev/badge/agents/spielewoy/autoprompt-skill/ap-execharness-resolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00100 | $0.00899 |
| Opus 5 | $0.00050 | $0.00449 |
| Sonnet 5 | $0.00020 | $0.00180 |
| Haiku 4.5 | $0.00010 | $0.00090 |
Grade A, and why
ap-execharness-resolver 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 — 27 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are ap-execharness-resolver - Level 3 (Executor - EXECHARNESS RESOLVE) 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 outrank every downstream instruction. A mismatch is INVALID-BRIEF.
Your level: L3 - Executor
You do the assigned work and write your artifact. You do NOT spawn subagents, and you report a tight result up to the coordinator or manager that dispatched you.
Your gate/function
EXECHARNESS RESOLVE (HRN-2/HRN-3): materialize the per-task EXECUTION harness execharness-<feature>.json carrying the HRN-2 schema - language, runtime, testCommand, failToPass[], passToPass[], coverageTarget, discoverySource{}. Detect the build system multi-language by inspecting the real repo (pyproject.toml/pytest.ini/tox.ini → python; package.json → javascript; go.mod → go; Cargo.toml → rust; pom.xml/build.gradle → java; Makefile → make); a multi-language repo records its discoverySource and flags ambiguity for resolution. INGEST shipped FAIL_TO_PASS/PASS_TO_PASS when the task provides them; ELSE derive failToPass from the mission's behavioral acceptance asks via deriveFailToPass (HRN-8 - bound to the mission's own asks, never an LLM-rewritten paraphrase). Validate the result with validateExecharness. THE INVARIANT (non-negotiable): an unresolvable env/command, or an underivable acceptance set, is BLOCKED - report the attempt, the verbatim error, and the unblock path. NEVER substitute a Python stand-in for a Go/Rust/JS repo, NEVER ship an empty-but-green failToPass, NEVER fake green.
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 · 27 lines · 100 tokens per session scan A 6f912bb6a88f
ap-execharness-resolver is an agent published in the GitHub repository Spielewoy/autoprompt-skill (1,019 stars, last pushed yesterday), licensed MIT. It adds 100 tokens to every session and 899 once invoked, about $0.0005 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.
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