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 ai-analyst-lab/ai-analyst --skill run-pipelinegit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/run-pipeline)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/run-pipeline"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/run-pipeline/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/ai-analyst-lab/ai-analyst/run-pipeline"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/run-pipeline.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 | $0.00027 | $0.00902 |
| Opus 5 | $0.00014 | $0.00451 |
| Sonnet 5 | $0.00005 | $0.00180 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
run-pipeline 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 2d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run an analytical workflow
Choose the amount of work that serves the request. A chart, investigation, validation report, and presentation have different completion conditions. Do not add presentation work to an analysis-only or validation-only request.
Inspect and propose
Read plans.md, agents/registry.yaml, and the selected worker contracts.
The registry declares coordination; contracts declare input requirements.
Do not infer required inputs from whatever happens to exist in global outputs.
Prepare a request JSON under working/requests/ with:
plan: an existing named plan.variables: concrete filename placeholder values.bindings: each worker's input values or explicit producer references.output_paths: exact paths replacing wildcard/dynamic output declarations.external_dependencies: input names replacing omitted producers.approval_gates: required approvals withid,after, andbefore.context: the exact analyticalquestion, plus optionalworker_questionswhen a worker needs a narrower framing.
For produced inputs use {"from": "worker.result"}. The first registered output
is result; later outputs are artifact_2, etc. Inspect the registry before
selecting one. For existing files supply an exact path, computed sha256,
and a purpose explaining why the file suits this question. Evaluate dataset,
scope and age as well: a hash establishes identity, not analytical suitability.
Never invent missing data, credentials, meaning, or approval. Ask only for inputs that cannot be safely supplied from the request and verified context.
When context.question is present, the controller builds a deterministic manifest and bounded
bundle for each worker from the frozen run snapshot. It attaches both as explicit inputs and blocks
on trusted-definition conflicts. The bundle records supply. Workers must cite relevant context item
IDs, and downstream validation still checks whether the work applied them.
Compile before executing
What ships with it
1 file 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.
- 2d ago First seen · 104 lines · 27 tokens per session scan A aa38927e0e8b
run-pipeline is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 902 once invoked, about $0.0001 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 2026-09-12.
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