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/existential-birds/beagle/review-structurenpx skills add existential-birds/beagle --skill review-structuregit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/existential-birds/beagle/review-structure)<a href="https://agentmods.dev/skills/existential-birds/beagle/review-structure"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/review-structure.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 | $0.00048 | $0.03015 |
| Opus 5 | $0.00024 | $0.01507 |
| Sonnet 5 | $0.00010 | $0.00603 |
| Haiku 4.5 | $0.00005 | $0.00301 |
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 5d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structural-Maintainability Review
Use this skill for an unusually strict review focused on implementation quality, maintainability, abstraction quality, and codebase health.
Above all, this skill should push the reviewer to be ambitious about code structure. Do not merely identify local cleanup opportunities. Actively search for "code judo" moves: restructurings that preserve behavior while making the implementation dramatically simpler, smaller, more direct, and more elegant.
The structural lens is repo-wide: read and search any file in the codebase as needed to judge whether canonical helpers already exist, whether file-size budgets are honored, and whether the change makes the codebase easier or harder to live with.
Anti-confabulation (gate 0 — runs before every other gate)
Before issuing any verdict — flag, propose a restructuring, or assert a structural claim — you MUST echo the exact artifact you are judging, quoted from a source you read in this turn:
- For a code finding: the file:line plus the cited code, read freshly now (not recalled from earlier in the session).
- For a structural claim ("no canonical helper exists", "file exceeds 1 000 lines"): the search pattern + result or
wc -l/read-based count that backs it.
The artifact is the only source of truth. Never infer what you are reviewing from the branch name, the working directory, surrounding files, or recollection. If your mental model differs from the freshly read source, the source wins. A verdict issued without a same-turn echo of its target is invalid — emit the echo first, or do not emit the verdict.
This gate exists because an LLM under contextual priming will confidently flag code that is not in the file. It runs before the hard gates below.
Hard gates (sequence)
Advance only when each pass condition is objectively satisfied (artifact path, tool output, or labeled capture — not "I checked it mentally"):
| Gate | Pass condition |
|---|---|
| G1 — Changed-file list | git diff --name-only (or equivalent) returns a non-empty list or you exit with an explicit "no changed files" message; the list is recorded before any review step begins. |
| G2 — Full file reads | Every file in scope has been read in full; for each file you record the path and line count (e.g. src/foo.ts — 342 lines). Do not proceed to findings until all reads are logged. |
| G3 — Canonical-helper and 1k-line claims verified | Any finding that asserts "no canonical helper exists" must cite a codebase-search artifact showing the search pattern and result. Any finding that asserts a file exceeds 1 000 lines must cite a wc -l or read-based line-count artifact. Findings lacking these artifacts are blocked from the report. |
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.
- 5d ago First seen · 258 lines · 48 tokens per session scan A 176d6fb7575e
review-structure is a skill published in the GitHub repository existential-birds/beagle (79 stars, last pushed 25d ago), licensed Apache-2.0. It adds 48 tokens to every session and 3,015 once invoked, about $0.0002 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.
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…