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 uchicago-dsi/ai-sci-skills --skill run-provenancegit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-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/uchicago-dsi/ai-sci-skills/run-provenance)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/run-provenance"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/run-provenance/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/uchicago-dsi/ai-sci-skills/run-provenance"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/run-provenance.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 | $0.00073 | $0.02085 |
| Opus 5.5 | $0.00029 | $0.00834 |
| Sonnet 5.5 | $0.00015 | $0.00417 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
run-provenance 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 20d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Provenance
Provenance exists so a later reader can tell what produced an artifact and remake it. Everything below scales with what the artifact is for: a probe nobody will reuse owes very little, and a parent that other runs consume owes all of it.
Choose The Tier Before You Launch
Use three tiers so iteration stays fast without weakening durable evidence.
- Developer checks. Compile/import checks, config validation, dry runs, and disposable local mechanics probes may use uncommitted code and need no provenance artifact. A read-only diagnostic that consumes immutable inputs and writes nothing anything else will consume is this tier by default; it needs no deliberation about which tier applies.
- Exploratory scheduled smokes. Uncommitted code is allowed when the run root automatically records the full base commit SHA, exact command, config hash, environment identity, immutable data/parent hashes, working-tree status, and an exact binary patch or source snapshot covering every dirty or untracked execution path. Report these results only as preliminary, with that provenance. They may not become reusable parents or decision-grade evidence.
- Decision-grade, expensive, or reusable runs. Long training, final comparisons, promotion/retirement evidence, and artifacts consumed as parents must execute from a clean checkout pinned to a full 40-character commit SHA, with every invoked code and config path tracked and committed. A dirty shared checkout should use a clean commit-pinned worktree rather than blocking unrelated development.
For tier 3, generate the run README and machine-readable provenance before the first scientific kernel or optimizer update. Record the full commit SHA, exact command and config hash, environment identity, immutable data/parent manifest hashes, and clean execution status; the commit already determines the tree and transitive source contents. Fail closed when a field is absent or the execution checkout differs from the commit. Resume with the same commit and bound hashes; a code change requires a new commit and a new semantic run slug or explicitly versioned attempt. Generate this automatically rather than spending agent effort on long narrative prose.
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.
- 20d ago Changed · +7 lines 28d1726778a9
- 26d ago Changed · +35 lines fe6c087045bc
- 1mo ago First seen · 123 lines · 73 tokens per session scan A 6125bdce639a
run-provenance is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (20 stars, last pushed 4d ago), licensed MIT. It adds 73 tokens to every session and 2,085 once invoked, about $0.0003 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 2026-09-22.
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