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.
git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-SkillsWrote 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/brycewang-stanford/auto-empirical-research-skills/plan-checker)<a href="https://agentmods.dev/agents/brycewang-stanford/auto-empirical-research-skills/plan-checker"><img src="https://agentmods.dev/badge/agents/brycewang-stanford/auto-empirical-research-skills/plan-checker/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/agents/brycewang-stanford/auto-empirical-research-skills/plan-checker"><img src="https://agentmods.dev/badge/agents/brycewang-stanford/auto-empirical-research-skills/plan-checker.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 | $0.00064 | $0.07085 |
| Opus 5 | $0.00032 | $0.03542 |
| Sonnet 5 | $0.00013 | $0.01417 |
| Haiku 4.5 | $0.00006 | $0.00709 |
Grade A, and why
plan-checker 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 670 lines · 64 tokens per session scan A 67c3a8623899
plan-checker is an agent published in the GitHub repository brycewang-stanford/Auto-Empirical-Research-Skills (3,759 stars, last pushed 5d ago), with no licence file. It adds 64 tokens to every session and 7,085 once invoked, about $0.0003 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 2026-09-07.
Other agents, from other repositories
visualization-agent
Parses paper data and statistical results to generate publication-quality figure code in Python (matplotlib/seaborn) or R (ggplot2) formatted to APA 7.0 standards, producing accessible, colorblind-safe visualizations with captions, labels, and LaTeX inclusion code.
Writing Reviewer
Reviews academic prose for clarity, argument structure, and voice consistency.
tester
Testing sub-agent for the ck:test skill. Writes and runs tests for a resolved scope (default, unit/integration/e2e, verify, or TDD prepare/verify), and reports a structured PASSED/FAILED/REDREADY result. Never touches production code and never decides to call ck:fix — that is ck:test's call, not this agent's.
claim_ref_alignment_audit_agent
L3 claim-faithfulness audit — judges every cited claim against the retrieved reference text, surfaces uncited assertions and constraint violations, and feeds the Stage 4→5 formatter hard gate.
Vuln Analyst Agent
AI agent that applies CISA SSVC to live CVE data (EPSS, KEV, NVD, OSV) for defensible patch decisions.
architecture_diagram_worker_agent
Produces one assigned architecture/flow-diagram module as an editable, manifest-tracked asset via the [V:NATIVE] SVG route (or Mermaid when it converts without editability loss).