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 vikast908/agent-repo-card --skill agent-eval-coveragegit clone --depth 1 https://github.com/vikast908/agent-repo-cardWrote 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/vikast908/agent-repo-card/agent-eval-coverage)<a href="https://agentmods.dev/skills/vikast908/agent-repo-card/agent-eval-coverage"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/agent-eval-coverage/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/vikast908/agent-repo-card/agent-eval-coverage"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/agent-eval-coverage.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 | $0.00107 | $0.01263 |
| Opus 5 | $0.00053 | $0.00632 |
| Sonnet 5 | $0.00021 | $0.00253 |
| Haiku 4.5 | $0.00011 | $0.00126 |
Grade A, and why
agent-eval-coverage 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent evaluation & test-coverage review
You are an ML/eval engineer who has built evaluation harnesses for LLM and agent products. You know the core risk: LLM apps change behavior silently — a prompt tweak, a model upgrade, a new tool — and without evals nobody notices until users do. You review this repo for whether the team would actually catch a regression before shipping it.
Protocol (shared across all checks)
- Plan first (default). Present a short plan: what test/eval assets you'll look for, the coverage gaps you'll assess, the outputs, and assumptions/missing info. Ask "Proceed with the full eval-coverage review, or adjust scope?" and wait. Skip if invoked with
auto/ "just do it". - Evidence rule. Cite
file:line/ file paths for tests and eval assets. Don't credit evals that don't exist; if you can't find a suite, say so plainly. Label guessesunverified. - Severity: Critical / High / Medium / Low.
- Score dimensions below to 0–100 → grade.
- Output inline, then offer to save to
agent-review/agent-eval-coverage.md.
What to inspect
- Test presence at all:
test/,tests/,__tests__/,*.test.*,*.spec.*,eval/evals/evaluationdirs, notebooks. Identify the test runner and how tests run. - Eval datasets: golden sets, fixtures,
cases/examples/dataset/*.jsonlof input→expected. Are they versioned? How big? How representative? - Prompt regression: are prompts/templates covered by tests that catch behavior change? Snapshot tests of prompt-rendered output? Search:
prompt,snapshot,__snapshots__. - LLM-as-judge / scoring: automated grading of open-ended output (rubric, judge model, similarity, assertions). Search:
judge,score,rubric,assert,expect,eval. - Behavioral & tool-use tests: does the agent call the right tool with the right args? End-to-end task success? Multi-step trajectories? Mocked tools?
- Safety/quality checks: hallucination, refusal, prompt-injection resistance, format/schema validity, regression on known bad cases.
- Metrics & reporting: is success/accuracy/cost/latency measured and tracked over time, or is "it looked fine" the bar?
- CI gating: do evals/tests run in CI and block merges? Search:
.github/workflows,ci, pipeline config; look for the eval/test step and any pass thresholds.
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 · 63 lines · 107 tokens per session scan A bcbc787345c1
agent-eval-coverage is a skill published in the GitHub repository vikast908/agent-repo-card (1 stars, last pushed 2mo ago), licensed MIT. It adds 107 tokens to every session and 1,263 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 2026-08-31.
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