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 skills add Aperivue/medsci-skills --skill model-sourcinggit clone --depth 1 https://github.com/Aperivue/medsci-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/skills/aperivue/medsci-skills/model-sourcing)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/model-sourcing"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/model-sourcing/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/aperivue/medsci-skills/model-sourcing"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/model-sourcing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00169 | $0.02182 |
| Opus 5 | $0.00084 | $0.01091 |
| Sonnet 5 | $0.00034 | $0.00436 |
| Haiku 4.5 | $0.00017 | $0.00218 |
Grade A, and why
model-sourcing 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 10d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model-Sourcing Skill
Purpose
/architecture-zoo answers a literature question — which family of model suits this task. That
question has a stable answer. The next question does not: which concrete artifact do I run?
A repository, a revision, a checkpoint. That is a provenance question, and the two facts a
careful researcher usually checks are the two that cannot answer it.
The licence tells you whether you may use it. The citation count tells you whether others did. Neither tells you whether the number you are about to report means what you will say it means.
The failure this skill exists for is the quietest one in the lane. A method developed and tuned against a benchmark family gets evaluated by the next person on that same family, and the resulting figure reads like validation while sitting much closer to a training-set score. Nothing in the repository says so. The licence is clean, the paper is peer-reviewed and highly cited, the task matches, the code runs on your GPU. The conflict lives in the relationship between two facts that are documented in different places — what the model was developed on, and what you are about to evaluate it on — and it becomes visible only when they are written down side by side.
Writing them down side by side is what the dossier is for.
When to use
- You have a concrete candidate (a GitHub repo, a Hugging Face checkpoint, a paper's released weights) and are about to build a study on it.
- You are writing the Methods paragraph that says which model you used, and it has to survive a reviewer asking what it was trained on.
- You inherited a pipeline whose model came from somewhere nobody recorded.
When NOT to use
- Choosing an architecture family →
/architecture-zoo(archetypes and the task-to-architecture logic; deliberately not a live leaderboard). - Building the training repo →
/model-scaffold. Designing the validation study →/model-validation. Computing held-out metrics →/model-evaluation. - Documenting a model you built →
/model-card(Model Card + Datasheet). - Auditing your own dataset before modelling →
/profile-imaging. - Evaluating an LLM/multimodal system on a clinical task →
/mllm-eval(which owns pretraining-contamination of public benchmarks for that setting).
What ships with it
11 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.
- scripts/check_model_provenance_challenge/expected/clean.txt 186 B
- scripts/check_model_provenance_challenge/expected/defect.txt 1.8 KB
- scripts/check_model_provenance_challenge/expected/unstated.txt 652 B
- scripts/check_model_provenance_challenge/fixture/dossier_clean.json 839 B
- scripts/check_model_provenance_challenge/fixture/dossier_defect.json 626 B
- scripts/check_model_provenance_challenge/fixture/dossier_unstated.json 556 B
- scripts/check_model_provenance_challenge/problem.md 2.4 KB
- scripts/check_model_provenance_challenge/verify.sh 2.5 KB runs code
- scripts/check_model_provenance.py 12 KB runs code
- skill.yml 4.1 KB
- tests/test_model_provenance.sh 4.8 KB runs code
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.
- 10d ago First seen · 167 lines · 169 tokens per session scan A b2e27cafc578
model-sourcing is a skill published in the GitHub repository Aperivue/medsci-skills (291 stars, last pushed 3d ago), licensed MIT. It adds 169 tokens to every session and 2,182 once invoked, about $0.0008 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 2026-08-30.
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