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/initnpx skills add skyllwt/AutoSci --skill initgit clone --depth 1 https://github.com/skyllwt/AutoSciWhat 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 | $0.00019 | $0.04365 |
| Opus 5 | $0.00010 | $0.02183 |
| Sonnet 5 | $0.00004 | $0.00873 |
| Haiku 4.5 | $0.00002 | $0.00436 |
Grade A, and why
init 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 3d 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/init
Build a wiki from
raw/with deterministic source preparation, planner-guided discovery, provisional notes/web scaffolding, and parallel/ingestfan-out/fan-in.
Use these local references on demand:
references/prepare-and-discovery.md— prepare flow, final selection, fetch, and source-manifest rulesreferences/planner-policy.md— planner behavior and LLM trim expectationsreferences/parallel-ingest.md— worktree isolation, subagent prompt contract, merge, and cleanup
Inputs
topic(optional): research direction keywords; omit whenraw/already defines the seed set--no-introduction(optional): disable external discovery; use only when the user explicitly requests it- User-owned sources under
raw/papers/,raw/notes/,raw/web/
Outputs
wiki/scaffold and provisional pages (Summary, topics, ideas, concepts)raw/tmp/andraw/discovered/prepared sources- Final paper pages via parallel
/ingestsubagents .checkpoints/init-*.jsonmanifests for resume and replay- Updated
wiki/index.md,wiki/log.md,wiki/graph/* - Refreshed visualization artifacts:
wiki/.obsidian/graph.json(per-entity-type color groups) andwiki/canvases/*.canvas(best-effort, see Step 6). The interactive web Graph view is served bytools/serve.py(SPA), not regenerated as a standalone file.
Wiki Interaction
Reads
raw/papers/,raw/notes/,raw/web/.checkpoints/init-prepare.jsonand.checkpoints/init-sources.jsonfor resume, planning, and fan-outwiki/index.mdplus existingwiki/topics/,wiki/ideas/,wiki/concepts/,wiki/methods/for duplicate avoidance and scaffold alignment
Writes
wiki/scaffold and provisional pagesraw/tmp/andraw/discovered/wiki/index.md,wiki/log.md,wiki/graph/*.checkpoints/init-prepare.json,.checkpoints/init-plan.json,.checkpoints/init-sources.json, andinit-sessioncheckpoint metadata
Graph edges created
/inititself creates only scaffold-level edges when provisional pages need them- all paper-driven edges are delegated to
/ingest
What ships with it
3 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.
- 3d ago First seen · 283 lines · 19 tokens per session scan A 681c4c42ed05
init is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 4,365 once invoked, about $0.0001 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
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brainstorming
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auto-perf-optimize
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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
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