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/zth9/skills/initnpx skills add zth9/skills --skill initgit clone --depth 1 https://github.com/zth9/skillsWhat 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 | $0.00047 | $0.00559 |
| Opus 5 | $0.00023 | $0.00280 |
| Sonnet 5 | $0.00009 | $0.00112 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
researcher: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 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.
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.
What it actually says
Researcher Init
Initialize a research repository with the LLM Wiki structure for persistent knowledge accumulation.
Repository Structure
The initialized repository will have:
<repo>/
├── AGENTS.md # Wiki schema and conventions (canonical)
├── CLAUDE.md # -> AGENTS.md symlink
├── GEMINI.md # -> AGENTS.md symlink
├── raw/ # Source documents (each in its own directory)
│ ├── <source_id_1>/
│ │ ├── content.md
│ │ ├── metadata.json
│ │ └── assets/ # Source-specific images/files
│ └── <source_id_2>/
│ ├── content.md
│ └── metadata.json
└── wiki/
├── index.md # Content catalog
├── log.md # Operation log
├── sources/ # Source summary pages
│ └── <source_id>.md
├── entities/ # Entity pages (people, orgs, products)
│ └── <name>.md
└── concepts/ # Concept pages
└── <name>.md
Note: Each source gets a unique directory raw/<source_id>/ where source_id is <sanitized_title>_<6_random_chars>.
Workflow
-
Check current directory:
- If current directory is NOT a git repository → Initialize wiki structure in current directory (
.) - If current directory IS a git repository → Ask user for repository path (default:
./research)
- If current directory is NOT a git repository → Initialize wiki structure in current directory (
-
Run initialization script:
python scripts/init_repo.py <path> -
Confirm structure created and explain next steps
Path Selection Logic
Non-git directory (recommended for dedicated wiki repos):
- Initialize directly in current directory
- The entire directory becomes the wiki repository
- No subdirectory needed
Git repository (for project-embedded wikis):
- Create
./researchsubdirectory by default - Keeps wiki separate from project code
- User can specify alternative path if needed
Next Steps
After initialization:
- Use
/researcher:research <source>to ingest sources - Use
/researcher:consult <query>to query knowledge
What ships with it
1 file 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.
- 2d ago First seen · 70 lines · 47 tokens per session scan A 107df3a9ab9c
researcher:init is a skill published in the GitHub repository zth9/skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 559 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 2026-08-31.
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…