Skill authoring

Skill LoogacyStudio/skills/.github/skills/skill-authoring

This repository stores reusable agent skills, repo level benchmark workflow material, and plugin bundles for coding agents.

Install

npx -y skills add LoogacyStudio/skills --skill skill-authoring

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

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Use when a repo customization or governance artifact needs a controlled update, review, or new scaffold, especially for skills, prompts, agents, hooks, instructions, learning notes, or change-log discipline.

SKILL.md

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Skill Authoring

This repo-local skill packages the repository's controlled self-evolution rules for authoring or reviewing customization artifacts.

Use it when the work is not just "write a file," but "write or evolve the right customization artifact in the right layer with the right review trail."

Use when

Use this skill when:

creating or updating SKILL.md files for this repository
creating or updating .prompt.md, .agent.md, .instructions.md, or hook policy artifacts
deciding whether a recurring issue belongs in docs, prompts, agents, skills, hooks, or evals
reviewing a governance-related artifact for boundary fit, overfitting, or hidden assumptions
extracting reusable learning from completed work
preparing change-log and approval-ready closeout notes

Do not use when

Do not use this skill when:

the task is a narrow benchmark artifact already covered by benchmark-specific skills
the task is a one-off code edit with no reusable governance implication
the request is pure implementation work with no customization or self-evolution question

Primary references

docs/ai-governance/skill-authoring-contract.md
docs/ai-governance/controlled-self-evolution-checklist.md
docs/ai-governance/controlled-self-evolution-plan.md
docs/ai-governance/controlled-self-evolution-task-list.md
docs/ai-governance/skill-change-log.md
docs/ai-governance/learning-notes.md
docs/ai-governance/hook-policy.md

Workflow

Frame the observed problem or request in plain language.
Decide whether a reusable change is needed at all.
Classify the smallest correct destination:
- docs
- instructions
- prompts
- agents
- skills
- hooks
- evals
If prompts route to custom agents, verify that any prompt-local output contract is explicit and overrides the agent fallback schema when both define structure.
State before / after behavior explicitly.
Name regression, overfit, and hidden-assumption risks.
Attach learning-note, eval, and change-log implications.
Preserve human approval gates for governance changes.

Output contract

Return:

the chosen destination
the smallest useful change
before / after behavior
risks and guardrails
eval or checklist follow-up
change-log / learning-note implications

Validation

A good result should:

choose the correct layer before drafting changes
avoid broadening Layer 0 without evidence
avoid using hooks for reasoning problems
avoid using skills for deterministic enforcement problems
check routed prompt / agent output-contract precedence when both surfaces define structure
include review and closeout implications

Common pitfalls

treating every awkward task as proof a new skill is needed
hiding important behavior in unreferenced scripts or notes
skipping the change-log because "the change is obvious"
widening always-on context when a prompt or agent would suffice
forgetting that routed prompts may carry explicit output contracts that must override an agent fallback schema

Completion rule

This skill is complete when the proposed or reviewed customization change is correctly classified, minimally scoped, and tied to review, validation, and closeout discipline.