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Agent skill

Run Pipeline

by ai-analyst-lab in ai-analyst-lab/ai-analyst

Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

MITAuto-check passed

Install Run Pipeline

skills CLI
$ npx skills add ai-analyst-lab/ai-analyst --skill run-pipeline -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ai-analyst-lab/ai-analyst run-pipeline --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/run-pipeline .claude/skills/run-pipeline && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
run-pipeline
GitHub stars
304
Token cost
~1.2k tokens
SKILL.md length
543 words
Files
2
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

  • SKILL.md covers Inspect and propose, Compile before executing, Execute through the controller and Inspect and report
  • Calls python

What it does

Run Pipeline is an agent skill from ai-analyst-lab/ai-analyst. Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `plans.md`).

It works with Python. The repository describes itself as: AI Product Analyst — Claude Code-powered data analysis toolkit. The licence is MIT.

Example prompts

  • “/run-pipeline”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 52c0744. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Run Pipeline loads about 1.2k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 543 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ai-analyst-lab/ai-analyst at commit 52c0744, republished under its MIT licence (© ai-analyst-lab). 543 words, ~1,170 tokens.

Download SKILL.mdSave it as .claude/skills/run-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
run-pipeline
description
Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

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 with id, after, and before.
  • context: the exact analytical question, plus optional worker_questions when 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

Use the active project Python environment:

python
import json
from pathlib import Path
from helpers.pipeline.compile_plan import compile_named_plan
from helpers.pipeline.controller import Controller

root = Path.cwd()
request = json.loads(Path("working/requests/request.json").read_text())
definition, inputs = compile_named_plan(root, request)
print(json.dumps(definition, indent=2))

Replace the request path with the actual file. Review jobs, input bindings, handoffs, deliverables and stopping conditions before execution. dry-run=true ends here without creating a run or launching workers. Compilation is not model execution or proof of analytical correctness.

For custom workflows use the explicit definition format in docs/PIPELINE-CONTROLLER.md. A list of worker names alone is not a complete workflow contract.

Show full SKILL.md (212 more words)Show less

Execute through the controller

After the proposed scope is authorized:

python
run = Controller.create(root, definition, inputs)
print(run.directory)

Then invoke python -m helpers.pipeline.controller run EXACT_RUN_DIRECTORY. The controller uses Claude Code with claude-opus-4-6, a fresh process per job, normal permissions, and explicit output paths. Never bypass permissions or silently execute a blocked isolated job inline. Workers must not recursively invoke this skill or edit controller state.

Code owns readiness, bounded retries, artifact checks, status and completion. Query logging inherits a worker-specific directory. Only validated run-local artifacts enter the handoff ledger. This is logical isolation, not an OS sandbox.

Numeric historical checkpoints in plans are not executable approvals. New gates are explicit. Record an approval only after the named person approves the actual evidence; local actor strings do not authenticate identities.

Inspect and report

Read final status and actual artifacts. An optional failure produces a degraded run, even when a deliverable exists. Missing required evidence is not success. Structural checks do not establish analytical correctness. Apply the relevant analytical methods and preserve limitations.

Presentation workers retain their own chart, storytelling and export standards. This entry point does not impose those deliverables on unrelated plans.

Resume only the explicitly identified run using /resume-pipeline. Do not copy global artifacts into a run to make it look complete. See docs/PIPELINE-CONTROLLER.md for migration and current limitations.

© ai-analyst-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .claude/skills/run-pipeline of ai-analyst-lab/ai-analyst.

  • SKILL.md
  • plans.md

Open the folder on GitHubat commit 52c0744

Compare with similar skills

Run Pipeline next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Run Pipeline this skillai-analyst-lab/ai-analyst304—~1.2kAutomated safety check: PassMIT
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PDF Processinganthropics/skills180k47 repos~2kAutomated safety check: PassProprietary
NotebookLM Research AssistantPleasePrompto/notebooklm-skill7.8k14 repos~2.4kAutomated safety check: NotesMIT
Manim Video Productionbrowser-use/video-use29k6 repos~3kAutomated safety check: PassMIT
PPT Masterhugohe3/ppt-master59k1 repos~2.5kAutomated safety check: PassMIT

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Works with

Questions about Run Pipeline

What does Run Pipeline do?

Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller. Run Pipeline is an agent skill from ai-analyst-lab/ai-analyst. Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

How do I install Run Pipeline in Claude Code?

Run `npx skills add ai-analyst-lab/ai-analyst --skill run-pipeline -a claude-code`. Or copy the skill folder (.claude/skills/run-pipeline in ai-analyst-lab/ai-analyst) into .claude/skills/run-pipeline in your project. Claude Code loads it when a task matches its description.

How do I install Run Pipeline in Codex?

Run `npx skills add ai-analyst-lab/ai-analyst --skill run-pipeline -a codex`. Or copy the skill folder (.claude/skills/run-pipeline in ai-analyst-lab/ai-analyst) into .agents/skills/run-pipeline in your project. Codex loads it when a task matches its description.

Can I use Run Pipeline in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ai-analyst-lab/ai-analyst --skill run-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-pipeline, .gemini/skills/run-pipeline, .github/skills/run-pipeline and .opencode/skills/run-pipeline in your project.

What does Run Pipeline need to run?

Going by SKILL.md and its folder, Run Pipeline needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Run Pipeline access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Run Pipeline safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Run Pipeline use?

Run Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Run Pipeline use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Run Pipeline?

Skills that share tags, products or a category with Run Pipeline: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Run Pipeline?

ai-analyst-lab (a GitHub organization) maintains it in ai-analyst-lab/ai-analyst, which has 304 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on September 30, 2026.

Source: ai-analyst-lab/ai-analyst on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.