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/resetnpx skills add skyllwt/AutoSci --skill resetgit 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/reset)<a href="https://agentmods.dev/skills/skyllwt/autosci/reset"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/reset.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 | $0.00033 | $0.01089 |
| Opus 5 | $0.00016 | $0.00544 |
| Sonnet 5 | $0.00007 | $0.00218 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
reset 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/reset
Resets the wiki to a clean scaffold by scope. Designed for development iteration and recovery after a failed setup — not a routine operation.
Trigger
Manual: /reset --scope wiki / --scope raw / --scope log / --scope checkpoints / --scope all. Multiple scopes may be combined comma-separated: --scope wiki,log.
Inputs
--scope(required): one ofwiki— delete every*.mdunderwiki/<entity>/andwiki/outputs/, pluswiki/index.md,wiki/log.md, andwiki/graph/files. Preserves.gitkeepandwiki/CLAUDE.md.raw— delete every entry underraw/papers/,raw/discovered/,raw/tmp/,raw/notes/,raw/web/(except.gitkeep).log— resetwiki/log.mdto the empty header.checkpoints— clear batch state viaresearch_wiki.py checkpoint-clear.all— every scope above.
Outputs
- Cleared / reset files on disk.
- Console summary of deleted files and reset files.
Wiki Interaction
Reads
- All
wiki/<entity>/*.md(to enumerate the deletion plan). raw/<sub>/*(to enumerate raw deletions).
Writes
- Deletes
wiki/<entity>/*.md(preserves.gitkeep). - Rewrites
wiki/index.md,wiki/graph/*, optionallywiki/log.md. - Deletes
raw/<sub>/*(except.gitkeep).
Workflow
Pre-conditions: working directory contains wiki/, tools/. Set WIKI_ROOT=wiki/.
Step 1: Build the deletion plan (dry-run)
python3 tools/reset_wiki.py --scope <scope>
This prints a JSON plan listing every file that would be deleted or reset, without modifying anything. Display the plan to the user grouped by scope (wiki entity dirs, raw subdirs, log, checkpoints).
Step 2: Confirm with the user
Print the plan summary and ask for explicit confirmation:
About to delete N files and reset M files. Continue? [y/N]
If the user says no, exit. Never proceed without explicit approval — /reset is destructive and raw/ deletions are not tracked by git.
Step 3: Execute
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 · 113 lines · 33 tokens per session scan A 9a73a93f3145
reset is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 4d ago), licensed MIT. It adds 33 tokens to every session and 1,089 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
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brainstorming
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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…