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/skyllwt/autosci/checknpx skills add skyllwt/AutoSci --skill checkgit clone --depth 1 https://github.com/skyllwt/AutoSciWrote 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/skyllwt/autosci/check)<a href="https://agentmods.dev/skills/skyllwt/autosci/check"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/check.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 | $0.00032 | $0.02303 |
| Opus 5 | $0.00016 | $0.01151 |
| Sonnet 5 | $0.00006 | $0.00461 |
| Haiku 4.5 | $0.00003 | $0.00230 |
Grade A, and why
check 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/check
Scans the full wiki to detect structural, link, field, and graph health issues, and generates a tiered fix-recommendation report. Covers every entity type declared in
runtime/schema/entities.yaml(papers, concepts, topics, people, ideas, experiments, methods, Summary, foundations), plus graph edge / citation consistency. Highlights include: idea novelty-score plausibility, idea failure-reason completeness, experimentlinked_ideavalidity.
Inputs
- Full wiki directory (default
wiki/) - Optional:
--jsonflag (output JSON format viatools/lint.py) - Optional:
--fixflag (auto-fix deterministic issues) - Optional:
--fix --dry-run(preview fixes without applying them) - Optional:
--suggestflag (show recommendations for issues that cannot be auto-fixed)
Outputs
- Lint report (reported directly to the user)
- Optional file write:
wiki/outputs/lint-report-{date}.md
Wiki Interaction
Reads
wiki/papers/*.md— paper page fields and linkswiki/concepts/*.md— concept page fields and linkswiki/topics/*.md— topic page fields and linkswiki/people/*.md— people page fields and linkswiki/ideas/*.md— idea status, novelty_score, failure_reason, origin_gaps, target_venuewiki/experiments/*.md— experiment status, linked_idea, outcomewiki/methods/*.md— method type, source_papers, parent/child chainswiki/Summary/*.md— survey page fieldswiki/foundations/*.md— foundations (terminal — incoming-link checks only)wiki/graph/edges.jsonl— semantic graph edge consistency checkwiki/graph/citations.jsonl— bibliographic citation consistency checkwiki/index.md— cross-check page completeness
Writes
- Does not directly modify wiki content (reports only, unless
--fixis set) wiki/log.md— records lint result summary viatools/research_wiki.py log
Workflow
Pre-conditions: confirm the working directory is the wiki project root (directory containing wiki/, raw/, tools/).
Set WIKI_ROOT=wiki/.
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 · 192 lines · 32 tokens per session scan A ad192b90d281
check is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 5d ago), licensed MIT. It adds 32 tokens to every session and 2,303 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…