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/setupnpx skills add skyllwt/AutoSci --skill setupgit 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 | $0.00027 | $0.02384 |
| Opus 5 | $0.00014 | $0.01192 |
| Sonnet 5 | $0.00005 | $0.00477 |
| Haiku 4.5 | $0.00003 | $0.00238 |
Grade B, and why
setup scanned grade B with 2 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 2d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post('https://data.rag.ac.cn/api/register/sdk', json=payload, timeout=30) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post('https://data.rag.ac.cn/api/register/sdk', json=payload, timeout=30) 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.
/setup
Guides you through ΩmegaWiki's optional API key configuration. Reads your current
.env, shows what is and isn't configured, and helps you set up each key with clear explanations of what it does and how to get it. Safe to re-run at any time — only updates keys you choose to configure.
Inputs
- No arguments required
- Reads:
.env(current configuration state) - Reads:
config/setup-guide.md(reference for what each key does)
Outputs
- Updated
.envwith any newly configured keys - A summary of current configuration status
Wiki Interaction
Reads
- None (setup runs before any wiki exists)
Writes
- None (does not touch the wiki)
Workflow
Step 1: Read Configuration Reference
Read config/setup-guide.md to load the complete reference for all configurable keys,
including what each does, which skills use it, how to get it, and fallback behavior.
Step 2: Detect Current Environment
Run the following to check what is already configured:
python3 -c "
import sys, os
sys.path.insert(0, 'tools')
try:
import _env
except Exception:
pass
keys = {
'SEMANTIC_SCHOLAR_API_KEY': 'Semantic Scholar',
'DEEPXIV_TOKEN': 'DeepXiv',
'LLM_API_KEY': 'Review LLM (API key)',
'LLM_BASE_URL': 'Review LLM (base URL)',
'LLM_MODEL': 'Review LLM (model)',
}
for k, label in keys.items():
v = os.environ.get(k, '').strip()
print(f'SET:{k}' if v else f'UNSET:{k}')
"
Also detect the Python environment and .venv status:
ls .venv/ 2>/dev/null && echo "venv:present" || echo "venv:absent"
python3 --version
Step 3: Show Configuration Status
Present a clear summary to the user, grouped by status:
ΩmegaWiki Configuration Status
================================
✓ ANTHROPIC_API_KEY — managed by Claude Code (claude login)
Recommended:
✗ Semantic Scholar — not set (citation expansion 3x slower — get free key)
Optional:
✗ DeepXiv — not set (semantic search unavailable)
✗ Review LLM — not set (cross-model review unavailable)
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.
- 2d ago First seen · 283 lines · 27 tokens per session scan B daa9b2c14798
setup is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 2,384 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). 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…