Borrowing it
Nothing to install: this file belongs to responsibleai/ASSERT. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/responsibleai/ASSERT/main/.claude/skills/run-assert-eval/SKILL.mdgit clone --depth 1 https://github.com/responsibleai/ASSERTWrote 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/responsibleai/assert/run-assert-eval)<a href="https://agentmods.dev/skills/responsibleai/assert/run-assert-eval"><img src="https://agentmods.dev/badge/skills/responsibleai/assert/run-assert-eval/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/responsibleai/assert/run-assert-eval"><img src="https://agentmods.dev/badge/skills/responsibleai/assert/run-assert-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Anti-Refusal · line 347 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- high Anti-Refusal · line 405 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00181 | $0.10614 |
| Opus 5.5 | $0.00072 | $0.04246 |
| Sonnet 5.5 | $0.00036 | $0.02123 |
| Haiku 4.5 | $0.00018 | $0.01061 |
Grade A, and why
run-assert-eval 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 — 684 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run an ASSERT evaluation
When to use
The user wants evidence of how their agent or model actually behaves. Not for fixing the agent — this skill finds and reports failures.
This skill has two entry modes:
- Run mode — no usable results exist yet. Establish a risk source (Steps 1-2):
Clarity (recommended) — an existing
.clarity-protocol/directory or a fresh discovery run driven through the Clarity MCP server (run_clarity), in-IDE — or risks the user supplies directly. Then turn each selected risk into an atomic config, run the pipeline (Steps 3-5), and report (Step 6). - Results Q&A mode — judged artifacts already exist under
artifacts/results/<suite>/<run>/and the user asks a question about them ("what are the highlights?", "top 3 examples of the worst failure mode?", "why did case X fail?"). Skip to Step 6 and answer THAT question from the artifacts — do not re-run, and do not fall back to the full canned report unless asked.
Choosing a risk source (Clarity recommended, never required)
Every eval starts from a risk. There are two supported sources, and the user chooses — never decide for them and never block on Clarity.
Path A — Clarity discovery (recommended — present it first, but never alone). Use an existing
.clarity-protocol/ or a fresh run via the Clarity MCP run_clarity tool.
Clarity's value is finding failure modes the user has not thought of, along
with severity and causal chains. Recommend it whenever the user is unsure what
to measure, is new to the agent, or wants coverage rather than one known bug.
Path B — user-supplied risks. The user names the risk themselves, as prose or by pointing at a PRD, design doc, threat model, red-team finding, incident report, risk assessment, or test plan. This is the right path when they already know what they want measured.
Both paths answer what to test for. Neither answers how. That is the job of the research procedure in Step 3: once a risk is named, it runs a literature review of how that risk has actually been evaluated and turns the findings into the test-set design. The output is not a restatement of the topic — it is how the topic manifests:
What ships with it
27 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.
- assets/dimension-review-template.md 4.7 KB
- assets/eval-config-template.yaml 6.9 KB
- clarity_intake.py 21 KB runs code
- plan_generation_path.py 5.0 KB runs code
- README.md 8.7 KB
- SETUP-CHECKLIST.md 5.6 KB
- smoke_slice.py 14 KB runs code
- tests/fixtures/clarity-protocol/failures/failure-01-user-disengagement.md 3.5 KB
- tests/fixtures/clarity-protocol/failures/failure-07-operational-risks.md 2.2 KB
- tests/fixtures/clarity-protocol/failures/failures.md 3.1 KB
- tests/fixtures/monolithic/failures/failures.md 1.9 KB
- tests/fixtures/synthetic/failures/failure-01-malformed.md 369 B
- tests/fixtures/synthetic/failures/failures.md 315 B
- tests/test_clarity_intake.py 11 KB runs code
- tests/test_dimension_review_validator.py 12 KB runs code
- tests/test_generation_path_planner.py 4.3 KB runs code
- tests/test_plan_generation_path.py 3.6 KB runs code
- tests/test_smoke_slice.py 15 KB runs code
- tests/test_validate_dimension_review.py 23 KB runs code
- validate_dimension_review.py 48 KB runs code
- workflows/diagnose-acs-delta.md 21 KB
- workflows/evaluation-intent-workflow.md 6.1 KB
- workflows/generation-isolation-workflow.md 4.2 KB
- workflows/govern-and-remeasure.md 41 KB
- workflows/iterative-dimension-workflow.md 9.8 KB
- workflows/measure-clarity-failures.md 30 KB
- workflows/research-eval-dimensions.md 41 KB
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 Changed · +111 lines · +28 tokens per session dd757db88d08
- 1mo ago First seen · 573 lines · 153 tokens per session scan A b1c7a5e5fbe5
run-assert-eval is a skill published in the GitHub repository responsibleai/ASSERT (352 stars, last pushed 4d ago), licensed MIT. It adds 181 tokens to every session and 10,614 once invoked, about $0.0007 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 2026-08-30.
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