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 skills add d-gangz/dgang-skills --skill review-structuregit clone --depth 1 https://github.com/d-gangz/dgang-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/d-gangz/dgang-skills/review-structure)<a href="https://agentmods.dev/skills/d-gangz/dgang-skills/review-structure"><img src="https://agentmods.dev/badge/skills/d-gangz/dgang-skills/review-structure/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/d-gangz/dgang-skills/review-structure"><img src="https://agentmods.dev/badge/skills/d-gangz/dgang-skills/review-structure.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00066 | $0.03060 |
| Opus 5 | $0.00033 | $0.01530 |
| Sonnet 5 | $0.00013 | $0.00612 |
| Haiku 4.5 | $0.00007 | $0.00306 |
Grade A, and why
review-structure 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 12d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Structure
You are auditing the user's repo so that you (Claude) can navigate it cold and operate inside it effectively. The user is non-technical — they don't search this repo, you do. Every principle below comes from how your own search tools actually work.
How you search a repo
Internalize this before auditing — every principle downstream is a consequence of these mechanics.
Read— load a known file by path. First move on entry: read rootCLAUDE.mdorREADME.md.Glob— pattern-match file paths. Case-sensitive, literal. e.g.**/CLAUDE.md,clients/*/profile.md.Grep— search file contents, usually scoped with--glob.Bash: ls / find— see directory shape, not content. e.g.find . -type d -maxdepth 2.CLAUDE.mdauto-loading:- Ancestor
CLAUDE.mdfiles (root + dirs above cwd) load at session start. - Subdirectory
CLAUDE.mdfiles load lazily, only when youRead/Edita file in that subtree. They do not fire onGlob,Grep, orls.
- Ancestor
The corollary: a subdirectory CLAUDE.md cannot rescue bad names. Naming carries navigation; CLAUDE.md only carries non-obvious context once you've already decided to open a file.
Principles (the audit checklist)
- Self-describing names. A cold reader (you) should infer content from the path alone.
data/says nothing;sales/pipeline.csvsays everything. Naming is your primary navigation aid because Glob/Grep see paths, not CLAUDE.md. - Lowercase kebab-case —
acme-corp/, notAcme Corp/orAcmeCorp/. This is mechanical, not aesthetic: spaces break shell commands, casing breaks Glob patterns. - Group by domain, not by file type. Folders are business areas (
sales/,clients/,operations/), not file kinds (templates/,data/,scripts/). Domain grouping lets you ignore irrelevant subtrees entirely; file-type grouping forces you to load everything. - Sibling folders mirror each other's shape. If every client folder has
profile.md,contacts.md,updates.md, thenGlob clients/*/profile.mdpulls all profiles in one call. If shapes vary, you have toReadeach folder to discover what's there. - Shallow + wide over deep + narrow. Three levels (root → domain → leaf) is comfortable. Five-deep generic nesting (
stuff/items/things/2024/q1/) gets lost. - Glossary for user-specific vocabulary. Keep an
agent-docs/glossary.mdfile (in anagent-docs/directory at the repo root, alongside other progressive-disclosure docs likearchitecture.md,conventions.md, etc.) for terms specific to the user's world that Claude can't infer — internal client codenames, industry acronyms, custom workflow names ("V2 brief", "the rolodex"), team shorthand. The rootCLAUDE.mdmust include this self-perpetuating rule:
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.
- 12d ago First seen · 198 lines · 66 tokens per session scan A f2e10584fe92
review-structure is a skill published in the GitHub repository d-gangz/dgang-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 3,060 once invoked, about $0.0003 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.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
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…
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…