fluxly Search skills… ⌘K 中文 Publish

ai-skill-improve

AI Verified
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI · meta · improvement · skills
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
100 / 100 · audit passed
Author / version / license
@arcasilesgroup · MIT
Token usage
Lean
Setup complexity
Plug-and-play
External API key
Not required
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
Python
Permissions
  • Read-only
  • Write / modify
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Output preview ai-skill-improve.preview 
---
name: ai-skill-improve
description: Improves an existing skill based on real project pain (prior eval corpora under .ai-engineering/…
category: ai
runtime: Python
---

# ai-skill-improve output preview

## PART A: Task fit
- Use case: Improves an existing skill based on real project pain (prior eval corpora under .ai-engineering/evals/, Engram cross-session observations, LESSONS.md, decision-store, instincts, proposals) by analysing the failure pattern, rewriting SKILL.md, and emitting the proposed delta as a PR comment only — no auto-merge. Trigger for 'improve this skill', 'improve /ai-plan', 'make /ai-review better', 'optimize all skills', 'batch improve skills'. Accepts a single skill name or 'all' for batch mode. Not for creating new skills from scratch; use /ai-scaffold instead. Not for platform audit; use /ai-ide-audit instead..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Quick start / Workflow / When to Use” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Improves an existing skill based on real project pain (prior eval corpora under .ai-engineering/evals/, Engram cross-session observations, LESSONS.md, decision-store, instincts, proposals) by analysing the failure pattern, rewriting SKILL.md, and emitting the proposed delta as a PR comment only — no auto-merge. Trigger for 'improve this skill', 'improve /ai-plan', 'make /ai-review better', 'optimize all skills', 'batch improve skills'. Accepts a single skill name or 'all' for batch mode. Not for creating new skills from scratch; use /ai-scaffold instead. Not for platform audit; use /ai-ide-audit instead.”.
- **02** When the source has headings, the agent prioritizes “Quick start / Workflow / When to Use” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Improves an existing skill based on real project pain (prior eval corpora under .ai-engineering/evals/, Engram cross-session obs…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Quick start”, “Workflow”, “When to Use”, “Common Mistakes”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name ai-skill-improve directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Quick start / Workflow / When to Use” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

Powered by GitHub Discussions. Sign in with GitHub to comment, react, or subscribe.