Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ysyecust/everything-claude-code/agent-sortnpx skills add ysyecust/everything-claude-code --skill agent-sortgit clone --depth 1 https://github.com/ysyecust/everything-claude-codeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00058 | $0.01306 |
| Opus 5 | $0.00029 | $0.00653 |
| Sonnet 5 | $0.00012 | $0.00261 |
| Haiku 4.5 | $0.00006 | $0.00131 |
Grade A, and why
agent-sort scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-sort — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- DAILY inventory
- LIBRARY inventory
- install plan
- verification report
- optional
skill-libraryrouter 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:
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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 215 lines · 58 tokens per session scan A 07a787b554e7
agent-sort is a skill published in the GitHub repository ysyecust/everything-claude-code (43 stars, last pushed 15d ago), licensed MIT. It adds 58 tokens to every session and 1,306 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-sort, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…