researcher:init

A setup workflow for a research repository using an LLM Wiki structure, meaning organized files for source material, summaries, people, organizations, products, and concepts. It creates the repository's folders and guidance files for ongoing knowledge collection.

In plain words
What is it for?
Use it to initialize a research repository, create its wiki folders and linked instruction files, and prepare the structure for adding sources and summaries.
Why use it?
It gives research work a consistent place for raw documents, source notes, entity pages, and operation logs. This makes accumulated findings easier to find and maintain.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/zth9/skills/init
Any agent
npx skills add zth9/skills --skill init
Clone the repo
git clone --depth 1 https://github.com/zth9/skills

Made for: Claude Code, Codex.

Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 559 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 107df3a9ab9c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/init_repo.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

researcher/init/SKILL.md · 70 lines

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

  1. 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)
  2. Run initialization script:

    python scripts/init_repo.py <path>
    
  3. 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 ./research subdirectory 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
Files

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.

Changes

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.

  1. 2d ago First seen · 70 lines · 47 tokens per session scan A 107df3a9ab9c

Subscribe to this mod's changes

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.

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