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/.github/prompts/run-assert-eval.prompt.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/commands/responsibleai/assert/run-assert-eval)<a href="https://agentmods.dev/commands/responsibleai/assert/run-assert-eval"><img src="https://agentmods.dev/badge/commands/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/commands/responsibleai/assert/run-assert-eval"><img src="https://agentmods.dev/badge/commands/responsibleai/assert/run-assert-eval.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 | $0.00125 | $0.08492 |
| Opus 5.5 | $0.00050 | $0.03397 |
| Sonnet 5.5 | $0.00025 | $0.01698 |
| Haiku 4.5 | $0.00013 | $0.00849 |
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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run an ASSERT evaluation
You help the user run an end-to-end ASSERT evaluation. Risks come from Clarity (recommended) or directly from the user. You drive the existing Clarity MCP tools and assert-ai CLI — you do not reimplement Clarity's questioning or any pipeline logic.
Read AGENTS.md at the repository root for full orientation on the ASSERT project, terminology, and target selection.
When to use
The user wants evidence of how their agent or model actually behaves. This skill finds and reports failures — it is not for fixing the agent.
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 via the Clarity MCPrun_claritytool, driven in-IDE — or risks the user supplies directly. Then turn each selected risk into an atomic config, run the pipeline (Steps 3-5), then 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). An existing
.clarity-protocol/or a fresh run viarun_clarity. Clarity's value is finding failure modes the user has not thought of, plus severity and causal chains. Recommend it whenever the user is unsure what to measure 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, or risk assessment. Right when they already know what they want measured.
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 · 246 lines · 125 tokens per session scan A 98d658fcf5b0
run-assert-eval is a command published in the GitHub repository responsibleai/ASSERT (352 stars, last pushed 4d ago), licensed MIT. It adds 125 tokens to every session and 8,492 once invoked, about $0.0005 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 2026-09-07.
Other commands, from other repositories
playwright-test-heal
Run all my tests and fix the failing ones.
verify-bug
Post-merge UAT verification workflow. Walks JIRA reproduce steps, performs comparative audits (Before/After), attaches evidence to JIRA, and transitions status on PASS.
api-aqa-flow-execution-and-report-analysis
Phase 6 Execution & Report Analysis of api-aqa-flow (USER INTERACTION REQUIRED).
bugfix
Bug fix workflow: root cause analysis → user review → regression test + fix via TDD.
timeout-fix
A procedure for diagnosing and fixing tests that exceed their time limit by finding causes such as slow processing, hanging network calls, deadlocks, heavy setup, or leaked resources.
AGENTS
Run the current binary in a temporary test repository with a real PTY. Use tmux-cli when available, capture terminal output, and inspect -vv logs when behavior depends on timing or shell state. Avoid treating a piped command as proof of interactive behavior.