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 agentsope/SkillAlchemy --skill agentsop-test-fix-loopgit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-test-fix-loop)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-test-fix-loop"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-test-fix-loop/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/agentsope/skillalchemy/agentsop-test-fix-loop"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-test-fix-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 329 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 531 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 549 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 551 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 588 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 497 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00146 | $0.07743 |
| Opus 5.5 | $0.00058 | $0.03097 |
| Sonnet 5.5 | $0.00029 | $0.01549 |
| Haiku 4.5 | $0.00015 | $0.00774 |
Grade A, and why
agentsop-test-fix-loop scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 593 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test-Fix Loop · SOP
One-liner: The test result IS the next prompt. Wiring the verifier is 20% of the work; framing its output as a useful feedback message is 80%.
1. 何时激活 (Activation Rules)
Activate this skill when any of the following triggers fire:
- The user says "have the agent fix until tests pass", "run lint and tests
automatically", "iterate until green", or invokes
aider --auto-test,cline --yes, or an OpenHands-style headless agent. - The task has a verifiable success command: a non-zero exit code on failure (pytest, ruff, mypy, eslint, tsc, go test, cargo check, npm run build, make check, …).
- You're wrapping a code-editing LLM in a script/CI step and need to decide: when does the agent return?
- The agent just made an edit and the next message in the loop would be "here's what the verifier said".
Do not activate when:
- Success is subjective (writing prose, designing UX). The loop has no feedback signal worth replaying.
- The verifier is slow + interactive (full E2E suite, multi-min builds).
Either async-ify the loop, or run a fast subset (
pytest -x -k changed) in the loop and gate the slow suite at PR review. - The gate is human approval, not a machine check — use the HITL skill.
2. 核心心智模型 (Core Mental Model)
2.1 The test result IS the next prompt
The agent's next turn is conditioned almost entirely on the message you
inject between edit-N and edit-N+1. That message — formatted from
stdout, stderr, exit_code — is the prompt. The framework labels it
"tool result" or "verifier output" but mechanically it is a user-role message
the LM consumes verbatim.
⇒ Framing the feedback dominates the model choice. A 4000-line raw pytest dump prompts a worse fix than a 30-line "first failing test, traceback, the diff you just applied" digest, regardless of the model behind it.
2.2 Four primitives
+-----------------+ +-----------------+ +-----------------+ +-----------------+
| 1. Verifier | | 2. Capture | | 3. Format | | 4. Iteration |
| command | | (stdout + | | feedback | | bound |
| | | stderr + | | message | | |
| - pytest -x | | exit_code) | | - first error | | - max N tries |
| - ruff check | | - timeout cap | | - last K lines | | - escalate / |
| - mypy --strict | | - byte cap | | - drop noise | | commit / skip |
| - eslint . | | - kill on hang | | - keep colors=0 | | |
+-----------------+ +-----------------+ +-----------------+ +-----------------+
What ships with it
4 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.
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.
- 1mo ago First seen · 593 lines · 146 tokens per session scan A f488ba856d7f
agentsop-test-fix-loop is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 146 tokens to every session and 7,743 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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