empirical-pipeline-python
Skill0 starsChanged 2 months agoNo licenceView on GitHub
Python 实证执行流水线：把锁定的 Analysis Manifest 在 Python 中执行为可复现脚本、环境记录、日志、诊断、表图与 Run Manifest。仅当项目明确要求 Python 或已是 Python-native 时使用；需已有 Design Packet 与 Analysis Manifest，不用于选 estimand、追显著性或写正文。
In this repository55 other files it gives its agents.
- humanizer/AGENTS.mdAGENTS.md
- _pi-config/AGENTS.mdAGENTS.md
- ai-disclosureSkill
- causal-analysisSkill
- check-methodologySkill
- diagnose-introductionSkill
- did-analysisSkill
- discussion-reviewSkill
- distill-introduction-exemplarSkill
- distill-methods-exemplarSkill
- distill-paper-exemplarSkill
- distill-results-exemplarSkill
- distill-story-exemplarSkill
- distill-theory-exemplarSkill
- empirical-intakeSkill
- empirical-pipeline-stataSkill
- empirical-writeupSkill
- exploratory-data-analysisSkill
- grill-the-claimSkill
- handoffSkill
- humanizerSkill
- human-writingSkill
- huntington-klein-causal-designSkill
- intro-reviewSkill
- latex-tablesSkill
- literature-notes-obsidianSkill
- methods-reviewSkill
- miles-management-organization-theoriesSkill
- ml-analysisSkill
- notebooklm-pipelineSkill
- officecliSkill
- paper-reviewSkill
- paper-state-protocolSkill
- paper-story-contractSkill
- paper-writing-stackSkill
- polishSkill
- pollock-qcSkill
- proofreadSkill
- python-panel-dataSkill
- research-gap-diagnosisSkill
- results-reviewSkill
- reverse-validation-pipelineSkill
- revision-coachSkill
- run-empirical-researchSkill
- stata-data-cleaningSkill
- stataSkill
- theory-reviewSkill
- toc-reviewSkill
- tod-debateSkill
- wooldridge-econometricsSkill
- write-introductionSkill
- write-methodsSkill
- write-resultsSkill
- write-theorySkill
- writing-for-agentsSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 8 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Does it work?
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.