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/ohong/agent-skills/explainnpx skills add ohong/agent-skills --skill explaingit clone --depth 1 https://github.com/ohong/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ohong/agent-skills/explain)<a href="https://agentmods.dev/skills/ohong/agent-skills/explain"><img src="https://agentmods.dev/badge/skills/ohong/agent-skills/explain.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00096 | $0.01702 |
| Opus 5 | $0.00048 | $0.00851 |
| Sonnet 5 | $0.00019 | $0.00340 |
| Haiku 4.5 | $0.00010 | $0.00170 |
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 4d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/explain
Help the user build enough understanding to participate in the next loop, not merely approve or reject a diff. Treat the output as a guided walkthrough of agent-made work: background first, intuition before details, code in a sensible order, and explicit checks for understanding.
Operating Principles
- Ground every claim in the real repo state. Read the diff, surrounding code, tests, plans, PR text, commit messages, and relevant docs before explaining.
- Preserve the user's worktree. Do not modify code, regenerate files, or clean up artifacts unless the user explicitly asks for edits.
- Separate evidence from inference. Say "the diff shows", "the commit message says", "I infer", or "unknown" instead of inventing rationale.
- Explain to create participation. The goal is that the user can suggest the next change, spot a weak assumption, or discuss the design fluently.
- Prefer a literate diff over file-order narration. Group changes by concept, data flow, user flow, or decision, not alphabetically by path.
- Use small examples, diagrams, tables, and concrete before/after behavior when they reduce mental load.
Scope Discovery
-
Determine the change set.
- If the user names a PR, branch, commit, range, file, or worktree, use that scope.
- If no scope is named, inspect the current repo:
git status --short, current branch, default branch, merge base, uncommitted diff, and commits ahead of the base branch. - In multi-worktree repos, run
git worktree listbefore assuming the current checkout is the whole story. - If there is no Git repo or no clear change set, ask one concise question for the scope.
-
Gather source material.
- Diff/stat:
git diff --stat,git diff,git diff --cached, orgit diff <base>...HEAD. - History:
git log --oneline --decorate --graph --max-count=30and relevantgit showoutput. - Project intent: README, docs, plans, ADRs, issue links, PR description, TODOs, and agent notes.
- Verification: tests run, snapshots, build logs, lint results, CI status, manual browser checks, and any failing commands.
- Surrounding implementation: call sites, types, tests, migrations, config, and previous patterns the change builds on or breaks.
- Diff/stat:
What ships with it
2 files 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.
- 4d ago First seen · 134 lines · 96 tokens per session scan A caf663a248e6
explain is a skill published in the GitHub repository ohong/agent-skills (3 stars, last pushed 4d ago), licensed MIT. It adds 96 tokens to every session and 1,702 once invoked, about $0.0005 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 2026-08-31.
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…