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/penwyp/claudepreference/explainnpx skills add penwyp/ClaudePreference --skill explaingit clone --depth 1 https://github.com/penwyp/ClaudePreferenceWhat 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 | $0.00085 | $0.01050 |
| Opus 5 | $0.00043 | $0.00525 |
| Sonnet 5 | $0.00017 | $0.00210 |
| Haiku 4.5 | $0.00009 | $0.00105 |
Grade A, and why
explain 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain
Explain the target in repository context, not in isolation. Always connect the local code to the surrounding call chain, owning module, upstream inputs, downstream effects, and user-visible responsibility.
Prefer evidence from the codebase. If a claim depends on inference, label it clearly as inference.
Input Decision
- Classify the input: code snippet → locate symbols in repo; file path → read file and expand to callers/callees; symbol name or implicit target → search repo for best match.
- Resolve ambiguity before explaining. If multiple matches, explain the best match and mention the ambiguity briefly.
- Keep scope aligned: a single-function question does not need a subsystem tour.
Analysis Workflow
- Read the target artifact.
- Expand one layer outward.
- Check who imports it, who calls it, what it calls, which config or env values feed it, and what outputs or side effects it produces.
- Place it in project structure.
- Identify whether it is an entry point, adapter, domain service, data model, UI component, CLI command, tool handler, scheduler job, test helper, or glue code.
- Extract the important parameters.
- Prioritize constructor params, function args, config fields, env vars, callback hooks, flags, discriminators, and return objects.
- Explain why those parameters matter.
- State what each important parameter controls, where it comes from, typical values, and what behavior changes when it is absent or changed.
- Summarize the role.
- End with the target's responsibility in one sentence that a new contributor can keep in their head.
What To Explain
- The target's direct responsibility.
- Why it exists in this project.
- Where it sits in the call path or module graph.
- What invokes it and what it invokes.
- What data it consumes and produces.
- Which parameters are key and which are incidental.
- What would break or change if a key parameter changed.
- Whether it is framework boilerplate, business logic, integration glue, or cross-cutting infrastructure.
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 · 89 lines · 85 tokens per session scan A 03fe830b5dc8
explain is a skill published in the GitHub repository penwyp/ClaudePreference (137 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,050 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…