pandas-analysis
Skill0 starsChanged 15 days agoMITView on GitHub
Keep transformations explicit, typed, and reproducible.
In this repository99 other files it gives its agents.
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- api-designSkill
- api-docsSkill
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- code-reviewSkill
- content-strategySkill
- context-budgetSkill
- conversion-optimizationSkill
- copy-editingSkill
- copywritingSkill
- coverage-strategySkill
- cryptographySkill
- data-qualitySkill
- data-visualizationSkill
- debuggingSkill
- decision-logSkill
- design-systemSkill
- dockerSkill
- documentationSkill
- e2e-testingSkill
- email-marketingSkill
- etl-designSkill
- experiment-designSkill
- experiment-methodsSkill
- fact-checkingSkill
- feedback-loopSkill
- flaky-testsSkill
- forecastingSkill
- fuzz-testingSkill
- go-concurrencySkill
- grant-writingSkill
- incident-opsSkill
- incident-responseSkill
- injection-defenseSkill
- integration-testingSkill
- interview-prepSkill
- job-fitSkill
- kubernetesSkill
- legacy-codeSkill
- linux-troubleshootingSkill
- literature-reviewSkill
- llm-evaluationSkill
- marketing-analyticsSkill
- meeting-facilitationSkill
- ml-evaluationSkill
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- observabilitySkill
- onboardingSkill
- paid-adsSkill
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- peer-reviewSkill
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 31
No fabricated sources, data, or test results.
- Distinctive: line 32
If the task falls outside the method, say so and stop.
≈ “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.
Files
Pandas analysis
Use
Apply this when the task matches the description. Keep the output decision-useful and mark unknowns.
Method
- State the decision and the unit of analysis.
- Profile grain, coverage, missingness, and leakage.
- Choose a method and write its assumptions.
- Validate on a holdout or independent slice.
Focus: Pandas analysis.
Verification
- Numbers reproduce from the written query/code.
- Assumptions and exclusions are visible.
- State clearly what was not verified.
Boundaries
- This is a checklist, not proof of correctness.
- No fabricated sources, data, or test results.
- If the task falls outside the method, say so and stop.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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.