Results for “skill-repair”

Iteratively reviews and fixes Claude Code skill quality issues by running automated fix-review cycles using the skill-reviewer agent until they meet standards.

Iteratively improve a Claude Code skill using the skill-reviewer agent until it meets quality standards. Use when improving a skill with multiple quality issues, iterating on a new skill until it meets standards, or automated fix-review cycles instead of manual editing.

Detects generalizable learnings from problem-solving interactions and proposes skill updates to improve future performance.

Create new skills, modify existing ones, and measure their performance through iterative evaluation and benchmarking.

Use an independently assigned `review-agent` for post-authoring review of changed rd-skills routing registries, fixtures, mappings, or owner conflicts without repair. Skip product work and in-task global rerouting.

Use when an existing Antigravity skill has weak triggers, vague instructions, missing constraints, or poor structure and needs to be improved. Trigger on requests to rewrite, tighten, split, or review a skill for better routing and reuse.

Update or correct an existing skill file based on judge feedback or improved understanding.

Discover, evaluate, install, and wrap external agent skills from ClawHub registry, skillflag-compliant CLI tools, and GitHub repos. Use when asked to find new capabilities, install a skill, browse what's available, or package a local skill for sharing.

Simulator.Company skill registry specialist — the data-driven analogue of these built-in skills. Use when the user wants to RUN a saved playbook ("run skill", "use the … skill", "/skill <slug>", "is there a skill for …", "what skills do I have"), or to AUTHOR one ("create a skill", "save this as a skill / playbook", "teach simulator to …", "make a reusable procedure"). A skill is an actor of the `Skills` system form whose `description` holds a step-by-step procedure (which MCP tools to call, with concrete entity ids) for a workspace-specific task such as "create a smart contract" or "onboard a client". Activate on: "run skill", "use playbook", "is there a skill for", "what skills do I have", "save as skill", "create a skill", "teach simulator", "запусти скіл", "використай скіл", "є скіл для", "які скіли є", "збережи як скіл", "створи скіл", "навчи simulator", "запусти навык", "используй навык", "сохрани как навык", "создай навык".

Create new skills, modify and improve existing skills, and measure skill performance through iterative evaluation and benchmarking.

Analyzes development cycle and quality metrics to identify recurring issues, then generates additive patches to skill instructions to prevent them.

Sincroniza el registro central de skills con AGENTS.md y registry/AGENT_REGISTRY.md, detectando skills huérfanas o faltantes y auditando el ecosistema.

Create or update Cursor agent skills (SKILL.md). Use when authoring a new skill, updating an existing skill, or asking about SKILL.md structure and best practices.

Guides users through creating, editing, and optimizing agent skills, including drafting, testing, evaluating, and improving skill descriptions for better triggering.

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Audit an existing processkit skill against the 5 Skill Killers, the Agent Skills standard, and Anthropic's troubleshooting playbook, producing a categorized findings report and draft Gotchas.

Test a pipeline stage's skill file by running the stage WITH and WITHOUT the skill on the same input, comparing outputs, and proposing skill edits. Ryan Law principle 3 — recursive self-improvement. Run after any board complaint about a stage, and monthly per core stage.

Forge reviews its own skill updates, review history, and research outputs before publishing them.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

This skill should be used when the user wants to "improve a skill", "update skill based on feedback", "optimize skill after use", or says "自更新", "/自更新". Collects user feedback after using a skill and automatically updates that skill with improvements.

Gracefully retire a skill that is redundant, superseded, or no longer earning its place in the context window. Load when improve-skills finds a skill scoring 0-5/14 AND research confirms the domain is now handled natively by current models, when two skills have overlapping triggers and one subsumes the other, when the user asks to remove a skill, retire a skill, delete a skill, or clean up redundant skills, or when validate-skills flags a skill as a duplicate trigger risk. Handles removal cleanly: updates all callers, removes from AGENTS.md, updates README, and archives rather than deletes so the skill can be recovered if needed.

Inspects a skill's SKILL.md and its observations/runs.md log, identifies failure patterns, and proposes a targeted amendment to improve the skill. Trigger on: "improve this skill", "fix this skill", "update this skill", "why does X keep failing", "this skill is wrong", "add this to the skill", or automatically when observations/<skill-name>/runs.md contains 3 or more failure entries. Outputs the amendment as a diff the user can review before applying. Records the amendment rationale in observations/<skill-name>/runs.md after user confirmation.

Audit, improve, and compress every skill in the repo using live research. Load when the user asks to improve skills, audit the skill library, upgrade existing skills, refresh with new research, do a skill health check, or says "improve all skills", "update the skill library", "skill audit", or "run an improvement pass". Applies live domain research, fixes structural gaps, checks for skill linking opportunities, then rewrites and resizes each skill. Supports TARGETED mode (`TARGET=<skill> [SKIP_RESEARCH=true]`) for single-skill fixes, including learn-from-chat restructure escalations. All skills are in scope including meta skills.

Write a skill, or forge a script tool, so a solved problem survives.

Refines an existing skill in place with minimal diffs, using evidence from usage and session mining to improve triggers, workflow, metadata, and resources.

Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.

Identify skill coverage gaps and improvement opportunities. Use when analyzing missing skills for a task, creating skill gap issues, evaluating skill effectiveness, or refining skill progressive disclosure.

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

>- Skill Refiner, the eval-guided improvement loop for SKILL.md files. Runs the bootstrap, score, propose, apply, and status cycle as a thin wrapper over the published @intentsolutions/refiner CLI, proposing safe, minimal, bounded SKILL.md edits and accepting an edit only when a held-out eval score strictly improves with no regression on any other case. Ships a 3-layer cost-tiered hook architecture (sinker, line, hook) that gates skill quality at edit time, end of turn, and commit time. Use when improving an existing skill, refining a SKILL.md against measured behavior, bootstrapping an eval set for a skill, or gating skill edits before they ship. Trigger with "/j-rig", "refine this skill", "bootstrap an eval set", "propose a skill edit", "promote the candidate", or "skill refiner status".

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.