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 echoomegaprime/echo-qcoder --skill qcoder-self-evaluatorgit clone --depth 1 https://github.com/echoomegaprime/echo-qcoderWrote 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/echoomegaprime/echo-qcoder/qcoder-self-evaluator)<a href="https://agentmods.dev/skills/echoomegaprime/echo-qcoder/qcoder-self-evaluator"><img src="https://agentmods.dev/badge/skills/echoomegaprime/echo-qcoder/qcoder-self-evaluator/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/echoomegaprime/echo-qcoder/qcoder-self-evaluator"><img src="https://agentmods.dev/badge/skills/echoomegaprime/echo-qcoder/qcoder-self-evaluator.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 | $0.00085 | $0.00461 |
| Opus 5.5 | $0.00034 | $0.00184 |
| Sonnet 5.5 | $0.00017 | $0.00092 |
| Haiku 4.5 | $0.00009 | $0.00046 |
Grade A, and why
qcoder-self-evaluator 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 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.
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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QCoder Self Evaluator
Expected input
Define the immutable candidate, baseline, acceptance thresholds, authorized target when applicable, permitted probe families, time and request budgets, and artifact destination.
Evaluation sequence
- Freeze candidate identity and record environment, model route, code revision, dataset revision, and configuration.
- Run deterministic unit, integration, protocol, security, and packaging checks before model-judged evaluations.
- Use the repository's labelled golden prompts for activation, tool choice, arguments, false positives, false negatives, and prohibited behavior.
- Use QCoder's local golden evaluator for repeatable model or agent comparisons. Promptfoo remains a pinned reference but is withheld from installation while its current dependency audit contains reachable high-severity findings; never weaken the audit gate to install it.
- Route Garak model probes and Nuclei application validation through isolated, scoped sidecars. For Nuclei or any active target traffic, switch to the pentester role and validate authorization first.
- Compare against the recorded baseline. Fail closed on unsafe activation, secret leakage, authorization bypass, destructive mislabelling, or a required deterministic gate.
- Save redacted machine-readable results and a human summary. A model grader may supplement but never replace executable evidence.
Recovery
Classify failures as product defect, test defect, environment defect, upstream drift, or external blocker. Repair only the owning layer, rerun the failed gate, then rerun the full ladder before release.
Stop conditions
Stop on target-scope mismatch, secret-bearing untrusted evaluation content, uncontrolled remote generation, missing immutable identity, resource exhaustion, or a doctrine hard limit.
Output format
Report candidate and baseline IDs, gates and counts, adversarial results, regressions, residual risk, artifact hashes, and promotion or rejection decision.
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 · 33 lines · 85 tokens per session scan A 8ac33667017f
qcoder-self-evaluator is a skill published in the GitHub repository echoomegaprime/echo-qcoder (0 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 461 once invoked, about $0.0003 per session on Opus 5.5. 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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