agent-sort
Agent-sort is a skill for creating project-specific ECC configurations by analyzing actual repository code to classify components into DAILY (always-loaded) versus LIBRARY (searchable but not default-loaded) buckets. Use it when a project needs a trimmed ECC surface matched to its specific language, framework, and workflow rather than loading the full bundle, with each classification decision backed by concrete evidence from the codebase such as file types, package managers, and build configurations.
git clone --depth 1 https://github.com/affaan-m/ECC /tmp/agent-sort && cp -r /tmp/agent-sort/.agents/skills/agent-sort ~/.claude/skills/agent-sortSKILL.md
# Agent Sort Use this skill when a repo needs a project-specific ECC surface instead of the default full install. The goal is not to guess what "feels useful." The goal is to classify ECC components with evidence from the actual codebase. ## When to Use - A project only needs a subset of ECC and full installs are too noisy - The repo stack is clear, but nobody wants to hand-curate skills one by one - A team wants a repeatable install decision backed by grep evidence instead of opinion - You need to separate always-loaded daily workflow surfaces from searchable library/reference surfaces - A repo has drifted into the wrong language, rule, or hook set and needs cleanup ## Non-Negotiable Rules - Use the current repository as the source of truth, not generic preferences - Every DAILY decision must cite concrete repo evidence - LIBRARY does not mean "delete"; it means "keep accessible without loading by default" - Do not install hooks, rules, or scripts that the current repo cannot use - Prefer ECC-native surfaces; do not introduce a second install system ## Outputs Produce these artifacts in order: 1. DAILY inventory 2. LIBRARY inventory 3. install plan 4. verification report 5. optional `skill-library` router if the project wants one ## Classification Model Use two buckets only: - `DAILY` - should load every session for this repo - strongly matched to the repo's language, framework, workflow, or operator surface - `LIBRARY` - useful to retain, but not worth loading by default - should remain reachable through search, router skill, or selective manual use ## Evidence Sources Use repo-local evidence before making any classification: - file extensions - package managers and lockfiles - framework configs - CI and hook configs - build/test scripts - imports and dependency manifests - repo docs that explicitly describe the stack Useful commands include: ```bash rg --files rg -n "typescript|react|next|supabase|django|spring|flutter|swift" cat package.json cat pyproject.toml cat Cargo.toml cat pubspec.yaml cat go.mod ``` ## Parallel Review Passes If parallel subagents are available, split the review into these passes: 1. Agents - classify `agents/*` 2. Skills - classify `skills/*` 3. Commands - classify `commands/*` 4. Rules - classify `rules/*` 5. Hooks and scripts - classify hook surfaces, MCP health checks, helper scripts, and OS compatibility 6. Extras - classify contexts, examples, MCP configs, templates, and guidance docs If subagents are not available, run the same passes sequentially. ## Core Workflow ### 1. Read the repo Establish the real stack before classifying anything: - languages in use - frameworks in use - primary package manager - test stack - lint/format stack - deployment/runtime surface - operator integrations already present ### 2. Build the evidence table For every candidate surface, record: - component path - component type - proposed bucket - repo evidence - short justification Use this format: ```text skills/frontend-patterns | skill | DAILY | 84 .tsx files, next.config.ts present | core frontend stack skills/django-patterns | skill | LIBRARY | no .py files, no pyproject.toml | not active in this repo rules/typescript/* | rules | DAILY | package.json + tsconfig.json | active TS repo rules/python/* | rules | LIBRARY | zero Python source files | keep accessible only ``` ### 3. Decide DAILY vs LIBRARY Promote to `DAILY` when: - the repo clearly uses the matching stack - the component is general enough to help every session - the repo already depends on the corresponding runtime or workflow Demote to `LIBRARY` when: - the component is off-stack - the repo might need it later, but not every day - it adds context overhead without immediate relevance ### 4. Build the install plan Translate the classification into action: - DAILY skills -> install or keep in `.claude/skills/` - DAILY commands -> keep as explicit shims only if still useful - DAILY rules -> install only matching language sets - DAILY hooks/scripts -> keep only compatible ones - LIBRARY surfaces -> keep accessible through search or `skill-library` If the repo already uses selective installs, update that plan instead of creating another system. ### 5. Create the optional library router If the project wants a searchable library surface, create: - `.claude/skills/skill-library/SKILL.md` That router should contain: - a short explanation of DAILY vs LIBRARY - grouped trigger keywords - where the library references live Do not duplicate every skill body inside the router. ### 6. Verify the result After the plan is applied, verify: - every DAILY file exists where expected - stale language rules were not left active - incompatible hooks were not installed - the resulting install actually matches the repo stack Return a compact report with: - DAILY count - LIBRARY count - removed stale surfaces - open questions ## Handoffs If the next step is interactive installation or repair, hand off to: - `configure-ecc` If the next step is overlap cleanup or catalog review, hand off to: - `skill-stocktake` If the next step is broader context trimming, hand off to: - `strategic-compact` ## Output Format Return the result in this order: ```text STACK - language/framework/runtime summary DAILY - always-loaded items with evidence LIBRARY - searchable/reference items with evidence INSTALL PLAN - what should be installed, removed, or routed VERIFICATION - checks run and remaining gaps ```
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
>
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
>
Build a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.
Bun as runtime, package manager, bundler, and test runner. When to choose Bun vs Node, migration notes, and Vercel support.
>
Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.