agentleFS
py-econometricspy-econometrics/pyfixest/.agents/skills/implementation-strategy/SKILL.md

implementation-strategy

Skill363 starsChanged 12 days agoMITView on GitHub

Choose the implementation and numerical evidence for pyfixest estimator, inference, formula, post-estimation, public API, or shared-core changes. Includes invariant refactors; excludes explanations and prose-only edits.

6 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

Implement a pyfixest change

Find the nearest implementation, its callers, and its tests. Use the architecture seams to choose where the change belongs. Apply the scope policy.

Resolve the decisions that affect this change:

  • The user-visible outcome or invariant, and the simplest implementation that preserves the relevant contracts. Read "Stable core" for contract changes; an already-authorized design does not need another approval.
  • Affected support paths under result boundaries. New estimators need a complete support matrix; existing features need only the affected paths reconsidered. Reject unsupported combinations explicitly.
  • For numerical work, an independent reference under reference selection, and whether results must stay invariant. Read "Release contract" for the applicable baseline check; do not silently turn a refactor into a behavior fix.
  • Necessary exports, user documentation, and compatibility decisions.

For a small fix, these decisions can be a brief explanation in the ongoing work. Write a separate plan only when it helps coordinate substantial work or the user asks for one. Implement once the material choices are clear; reuse settled decisions. Verify through change-verification and review the final diff through pr-review, without restarting the workflow at handoff.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.