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 oaslananka/zaptrace --skill zaptrace-benchmark-evaluationgit clone --depth 1 https://github.com/oaslananka/zaptraceWrote 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/oaslananka/zaptrace/zaptrace-benchmark-evaluation)<a href="https://agentmods.dev/skills/oaslananka/zaptrace/zaptrace-benchmark-evaluation"><img src="https://agentmods.dev/badge/skills/oaslananka/zaptrace/zaptrace-benchmark-evaluation/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/oaslananka/zaptrace/zaptrace-benchmark-evaluation"><img src="https://agentmods.dev/badge/skills/oaslananka/zaptrace/zaptrace-benchmark-evaluation.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 | $0.00027 | $0.00505 |
| Opus 5 | $0.00014 | $0.00253 |
| Sonnet 5 | $0.00005 | $0.00101 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
zaptrace-benchmark-evaluation 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZapTrace Benchmark Evaluation
Use this skill for deterministic ZapTrace benchmark and agent-evaluation workflows against local, committed fixtures.
When to use
Use this skill for:
- Running the public synthesis benchmark contract
- Evaluating a bounded agent scenario corpus
- Reviewing benchmark scorecards and convergence evidence
- Comparing results across source revisions
- Preparing machine-readable benchmark evidence for review
Do not use benchmark success to claim general EDA superiority, fabrication readiness, physical correctness, or independent third-party reproduction.
Required inputs
Collect:
- Benchmark or scenario identifier
- Committed fixture/corpus path
- Source revision
- Grader and tool versions
- Expected output and policy files
- Requested comparison baseline, if any
Workflow
- Confirm the fixture and policy are committed and integrity-checked.
- Use
synthesis_benchmarkfor the public MCP benchmark surface when it matches the requested task. - Use
synthesize_board_scoreonly for a bounded preview score; do not commit or export generated state implicitly. - For the twelve-scenario agent corpus, run
python scripts/ci_agent_evaluation.pywith explicit corpus, policy, JSON output, Markdown output, and artifacts directory arguments. - Preserve raw and normalized evidence, source revision, environment identity, fixture hashes, and grader versions.
- Compare only compatible benchmark versions and normalized fields.
- Report blocked, skipped, unsupported, and human-review-required outcomes without converting them to passes.
Quality checks
A valid benchmark report must include:
- Benchmark/corpus identity
- Source revision
- Fixture and policy hashes
- Tool and grader versions
- Scenario/task results
- Generated artifact hashes
- Stop reasons and skipped checks
- Comparison limitations
- Independent-reproduction status
Failure modes
Stop and report clearly when:
- Fixtures or policies are dirty, missing, or unpinned
- The requested score lacks source or grader identity
- Results come from incompatible benchmark versions
- External tools are unavailable and the check cannot be reproduced
- A repository-controlled rerun is presented as independent third-party evidence
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 · 78 lines · 27 tokens per session scan A 1579c2349c0a
zaptrace-benchmark-evaluation is a skill published in the GitHub repository oaslananka/zaptrace (0 stars, last pushed 5d ago), licensed MIT. It adds 27 tokens to every session and 505 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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