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 Eliyce/paqad-ai --skill test-execution-feedback-loopgit clone --depth 1 https://github.com/Eliyce/paqad-aiWrote 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/eliyce/paqad-ai/test-execution-feedback-loop)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/test-execution-feedback-loop"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/test-execution-feedback-loop.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00885 |
| Opus 5 | $0.00015 | $0.00443 |
| Sonnet 5 | $0.00006 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
test-execution-feedback-loop 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 7d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Reads the structured verification evidence file produced by the verifier, and for every entry in gates[].failures[] proposes the smallest change that would make that test pass. Each proposal is anchored to a specific file, line, AC id, and root-cause hypothesis — no prose-only suggestions and no fixes that work around the test.
The point is to collapse the typical fix-test-rerun-repeat loop from two turns into one: the model's next implementation turn can act on a structured proposal instead of re-reading raw test output.
Use This When
Use this immediately after the verifier reports overall_status: "fail" and before the next implementation turn begins. Run it whenever there are at least one failure in the evidence file. Skip in the fast lane unless explicitly requested.
Inputs
- Read the verification evidence at
verification_evidence_pathfirst; reject the run ifschema_versionis not1.0.x. - Read the acceptance criteria artifact when supplied so each failure's
ac_idcan be cross-checked. - Read the changed-file list to calibrate confidence (failures pointing at files outside the change set lower confidence to
low). - Read
references/fix-proposal-template.mdbefore drafting proposals so every proposal has the required fields.
Procedure
- Run
scripts/load-failures.sh [evidence-path]— emits one JSON object per failure, exits 1 if schema_version is unsupported. Iterate over those rows. - For each failure, read an excerpt around
file:lineto confirm the cited line still exists. - Cross-check
ac_idagainst the acceptance criteria artifact; markuntracedand lower confidence when the AC is missing. - Draft one proposal per failure (or one combined proposal when failures share a root cause) per
assets/output.template.md. - Confidence:
highonly when one-line, anchored to the change set, and AC is known. - Validate with
scripts/lint-output.sh.
Output Contract
- Return a heading named
Fix Proposals. - For each failure (or combined group), emit a third-level heading
### Failure {N}and the fields fromreferences/fix-proposal-template.md. - End with a summary line:
Total failures: {N} | Combined into {M} proposals | High-confidence: {H} | Defer to human: {D}. - When the evidence file has zero failures, return
Fix Proposals: none — verification passed.exactly.
What ships with it
5 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.
- 7d ago First seen · 80 lines · 29 tokens per session scan A 96f58d2b0973
test-execution-feedback-loop is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 885 once invoked, about $0.0001 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 2026-08-31.
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