Borrowing it
Nothing to install: this file belongs to adammatthewsteinberger/clippy-pet. 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/adammatthewsteinberger/clippy-pet/develop/.agents/skills/validate/SKILL.mdgit clone --depth 1 https://github.com/adammatthewsteinberger/clippy-petWrote 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/adammatthewsteinberger/clippy-pet/validate)<a href="https://agentmods.dev/skills/adammatthewsteinberger/clippy-pet/validate"><img src="https://agentmods.dev/badge/skills/adammatthewsteinberger/clippy-pet/validate.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 5.1 | $0.00057 | $0.00439 |
| Opus 5 | $0.00028 | $0.00219 |
| Sonnet 5 | $0.00011 | $0.00088 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
validate 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 7d 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.
What it actually says
Validate
Run the checks that apply to what actually changed in the working tree, per AGENTS.md and CONTRIBUTING.md's PR checklist. Don't run steps that don't apply — e.g. don't require a docs build for a change that only touched packaging/.
- Determine what changed:
git status --porcelainand/orgit diff --statagainst the target branch. - Always run the manifest/spritesheet validator if
pet.json,spritesheet.webp, or anything undersource/changed (or when in doubt — it's fast):. .venv/bin/activate 2>/dev/null || python3 -m venv .venv && . .venv/bin/activate && python3 -m pip install -r requirements-dev.txt make validate - If any
.shfile underscripts/orpackaging/changed:
(requiresmake lintshellcheck; report if it isn't installed rather than skipping silently) - If
docs/ormkdocs.ymlchanged:
This runspip install -r docs/requirements.txt make docsmkdocs build --strict, so broken internal links fail it. - If
CHANGELOG.md,VERSION, orCITATION.cffchanged, sanity-check by eye that they still agree with each other (the release guard enforces this strictly at tag time — see thereleaseskill). - Report which checks ran and their pass/fail result. Do not report the task as validated if a required check couldn't be run (e.g. missing
shellcheck) — say so explicitly instead of skipping it quietly.
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.
- 7d ago First seen · 29 lines · 57 tokens per session scan A 82cd4cb89032
validate is a skill published in the GitHub repository adammatthewsteinberger/clippy-pet (2 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 439 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 2026-08-31.
Other skills, from other repositories
bio-pose-validation
Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose…
copilotkit-contribute
Use when contributing to the CopilotKit open-source project — forking, cloning, setting up the monorepo, creating branches, running tests, and submitting pull requests against CopilotKit/CopilotKit.
bio-ortholog-inference
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when…
admet_genetic
ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.
bioprobench
Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.
alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…