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 aiopshwang/data-analysis-ml-agent-skills --skill shipping-reproducible-resultsgit clone --depth 1 https://github.com/aiopshwang/data-analysis-ml-agent-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/aiopshwang/data-analysis-ml-agent-skills/shipping-reproducible-results)<a href="https://agentmods.dev/skills/aiopshwang/data-analysis-ml-agent-skills/shipping-reproducible-results"><img src="https://agentmods.dev/badge/skills/aiopshwang/data-analysis-ml-agent-skills/shipping-reproducible-results/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/aiopshwang/data-analysis-ml-agent-skills/shipping-reproducible-results"><img src="https://agentmods.dev/badge/skills/aiopshwang/data-analysis-ml-agent-skills/shipping-reproducible-results.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 | $0.00062 | $0.00955 |
| Opus 5 | $0.00031 | $0.00477 |
| Sonnet 5 | $0.00012 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
shipping-reproducible-results 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 12d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shipping Reproducible Results
Ship an evidence-bearing result, not merely code that once ran. The package must connect the approved question to immutable inputs, an executable workflow, generated outputs, validation evidence, and an explicit handoff boundary.
Freeze the result contract
Before packaging, record:
- the approved question, population, unit of analysis, time window, and exclusions;
- the exact claims and acceptance criteria being delivered;
- which outputs are authoritative and which are exploratory;
- the source snapshot or retrieval contract, including access limitations;
- the intended execution environment and supported operating scope.
Do not silently expand a validated result from a sample to a population, from retrospective analysis to prospective use, or from one environment to another.
Build the provenance chain
Make each authoritative output traceable through:
source identity -> immutable snapshot or query -> transformation -> configuration
-> code revision -> environment -> model artifact -> evaluation -> published output
Record content hashes for immutable local artifacts and durable identifiers for remote sources. Preserve raw inputs when permitted; otherwise preserve the exact query, retrieval time, schema, row counts, access requirements, and a safe fixture sufficient to test the pipeline.
Separate source data, intermediate artifacts, final outputs, caches, and human edits. Never require a recipient to guess which file is canonical.
Make execution deterministic enough to audit
Provide one documented entry point from a clean state. Pin direct dependencies and runtime versions; record relevant system libraries and hardware. Seed every controllable source of randomness and document remaining nondeterminism rather than promising bitwise equality when the stack cannot provide it.
Packaging does not authorize restricted-data access, material compute, credential use, external transfer, or publication. Verify the relevant authority before those actions. When execution is not authorized or available, prepare the reproducible contract and label the unexecuted stages instead of running them.
What ships with it
4 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.
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.
- 12d ago First seen · 82 lines · 62 tokens per session scan A 73cb016c9e61
shipping-reproducible-results is a skill published in the GitHub repository aiopshwang/data-analysis-ml-agent-skills (12 stars, last pushed 16d ago), licensed MIT. It adds 62 tokens to every session and 955 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.
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