Menu
SkillsMP has collected 9 skills from chrisfoulon/LAD. Open a skill to review its source and details.
Showing 9 of 9 collected skills.
Final stage of the LAD feature workflow, run after a feature is merged. Condenses the working plan and context files into a durable record — appended to the project's existing ADR where one exists, or a new decision record where none does — harvests any new…
Assess whether the codebase still matches what the project says about itself — decision records, ADRs, documentation and unfinished plans. Reports drift and leftover work without fixing it. Use when picking up a project after time away, when you suspect the…
First stage of the LAD feature workflow. Clarifies the request, searches the codebase for work that already does the job, decides whether to integrate/enhance/rebuild, locates the relevant code and documentation as concrete references, and records a quality…
Code quality standards and known-lapse guardrails for Python work — docstring style, lint and coverage targets, component-appropriate testing strategy, test-data honesty, commit conventions, documentation structure, and token-efficient command patterns. Use…
Fourth stage of the LAD feature workflow. Runs final quality validation, checks that what was delivered matches what was planned, updates documentation, and prepares the commit or pull request. Use when a feature's implementation is complete and ready to be…
Third stage of the LAD feature workflow. Runs the test-driven implementation loop against the plan, with quality gates, regression checks, and periodic checkpoints for user approval. Resumable — detects where previous sessions stopped. Use when implementing a…
Focused technical-debt reduction — triage lint violations and code smells by actual impact, fix them in batched, tested increments, and record what was deliberately deferred. Use when asked to clean up code quality, reduce technical debt, address lint…
Second stage of the LAD feature workflow. Turns the kickoff context into a test-driven task plan, reviews that plan critically for gaps and risks, and splits it into sub-plans when it is too large to hold in one pass. Use after feature-kickoff, or when asked…
Systematically improve a failing or unreliable test suite. Runs the suite in chunks to get a real baseline, classifies failures by root cause, finds the shared causes behind clusters of failures, and fixes them in prioritised batches with regression checks…