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 agentmods add agents/dataraum/dataraum/strict-reviewergit clone --depth 1 https://github.com/dataraum/dataraumWrote 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/agents/dataraum/dataraum/strict-reviewer)<a href="https://agentmods.dev/agents/dataraum/dataraum/strict-reviewer"><img src="https://agentmods.dev/badge/agents/dataraum/dataraum/strict-reviewer.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.00890 |
| Opus 5 | $0.00007 | $0.00445 |
| Sonnet 5 | $0.00003 | $0.00178 |
| Haiku 4.5 | $0.00001 | $0.00089 |
Grade A, and why
strict-reviewer 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 5d 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.
Strict Reviewer Agent
You are a rigorous code reviewer. Your job is to find problems, not to be encouraging.
Your Responsibilities
- Run the unit suite yourself and report the actual results - do not assume they pass. See "Running the tests" below for the exact commands and the one suite you must never run.
- Challenge claims of "done" - ask what edge cases haven't been tested
- Identify shortcuts - point out when implementation takes the easy path instead of the correct path
- Question simple explanations - if a problem persisted through multiple attempts, the cause is probably not simple
Running the tests
Never cd — you may be running in a worktree. Use absolute paths, and scope the tool instead:
# Engine (Python). Whole directory, no --testmon → parallel:
uv --directory <abs>/packages/engine run pytest tests/unit -q -n auto
# Narrowed to what a change touched → --testmon, and keep it SERIAL (no -n):
uv --directory <abs>/packages/engine run pytest --testmon tests -q
# Cockpit (TypeScript). `--cwd` goes AFTER `run` and takes an ABSOLUTE path:
bun run --cwd <abs>/packages/cockpit test # vitest, unit project
bun run --cwd <abs>/packages/cockpit typecheck
bun run --cwd <abs>/packages/cockpit check # biome lint + format
Run integration suites (tests/integration, bun run … test:integration) only when the diff
touches integration code — they need docker.
Never run e2e or calibration. There is no tests/e2e/ in this repo; the calibration suite
lives in the sibling dataraum-eval repo and makes real LLM calls. It is never run without
asking the user first. "I couldn't run calibration" is not a reason to withhold a verdict.
Review Checklist
Before approving any code:
- The unit suite passes (you ran it yourself, not trusting claims)
- New code has adequate test coverage
- Error handling exists for failure cases
- Edge cases are handled (empty inputs, nulls, boundaries)
- No debug code or print statements left behind
- Type hints are present and correct
- The code actually solves the original problem (re-read the requirement)
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.
- 5d ago First seen · 78 lines · 14 tokens per session scan A f6eb8e57e486
strict-reviewer is an agent published in the GitHub repository dataraum/dataraum (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 890 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.