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/junmystery/agent-guidance-python/agent-sortnpx skills add JunMystery/Agent-Guidance-Python --skill agent-sortgit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWhat 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 | $0.01310 |
| Opus 5 | $0.00029 | $0.00655 |
| Sonnet 5 | $0.00012 | $0.00262 |
| 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
95% identical to agent-sort — 1 line 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 — 216 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 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 · 216 lines · 58 tokens per session scan A 51d5b461ebd9
agent-sort is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 1,310 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to agent-sort, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
common-feedback-reporter
Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.
common-exploit-verification
Enforce "No Exploit, No Report" policy with PoC construction standards, false-positive filtering, and evidence collection per vulnerability class across backend, frontend, and mobile. Use when validating security findings, constructing exploit proofs, filtering false positives, or writing pentest findings.
common-session-retrospective
Analyze conversation corrections to detect skill gaps and prepare targeted skill-library maintenance tasks. Use after any session with user corrections, rework, or retrospective requests. After finding correction loops, also load +common/common-learning-log to persist mistake entries to AGENTSLEARNING.md.
common-store-changelog
Generate user-facing release notes for the App Store and Google Play from git history (App Store <=4000 chars, Google Play <=500). Use when generating release notes, app store changelog, play store release, or "what's new" text for a mobile app.
typescript-language
Apply modern TypeScript standards for type safety and maintainability. Use when working with types, interfaces, generics, enums, unions, or tsconfig settings.
common-code-review
Conduct high-quality, persona-driven code reviews. Use when reviewing PRs, critiquing code quality, or analyzing changes for team feedback.