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Wiki Research Loop

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rohitg00
wiki-research-loop

Auto-grow a pro-workflow wiki by running a budget-capped BFS research loop over pluggable source fetchers (web, arXiv, GitHub). Each iteration pops a seed from the queue, fetches sources, drafts a wiki page, dedupes claims against existing pages, enqueues follow-up seeds. Halts on budget cap, depth cap, or convergence. Use when the user says "research <topic>", "grow the <slug> wiki", "auto-research", or wants a knowledge base that builds itself overnight.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namewiki-research-loop
Stars
2.9K
Forks
286
Bundled files
4
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Wiki Research Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/wiki-research-loop .claude/skills/wiki-research-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wiki Research Loop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wiki Research Loop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wiki Research Loop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wiki Research Loop

Driver that turns a wiki into an auto-grown knowledge base. Layers on top of wiki-builder and wiki-query.

Loop semantics

seed-queue (pending) → next-seed
  → fetch sources via plugins (web | arxiv | github)
  → extract claims
  → dedupe vs index (FTS5; later vector via 3.3.2)
  → compile new page or amend existing
  → upsert page (auto-FTS-index)
  → enqueue follow-up seeds (max-depth gate)
  → mark seed done
  → if budget OR convergence OR kill-switch → halt

Halt conditions (any one trips)

  • budget_usd exceeded (loop tracks per-fetcher cost estimate)
  • max_pages_per_run written
  • max_depth reached on every active branch
  • 3 consecutive pages add < 5 % new claims (convergence)
  • File ~/.pro-workflow/STOP exists (operator kill-switch)
  • wiki.config.md auto_research.enabled: false
  • Wiki private: true AND any non-local fetcher selected

Commands

node $SKILL_ROOT/scripts/research-loop.js run <slug> [--max-pages N] [--max-depth N] [--budget-usd 0.50] [--fetchers web,arxiv,github]
node $SKILL_ROOT/scripts/research-loop.js seed <slug> "<query>" [--depth 0] [--parent-id N]
node $SKILL_ROOT/scripts/research-loop.js seeds <slug> [--status pending|active|done|failed]
node $SKILL_ROOT/scripts/research-loop.js cancel <slug>
node $SKILL_ROOT/scripts/research-loop.js status

CLI flags override wiki.config.md for one run only.

Source fetchers

Pluggable. Each lives at scripts/source-fetchers/<name>.js. Interface:

js
module.exports = {
  name: 'web',
  match: (q) => true,                       // is this fetcher useful?
  estimateCost: (q) => ({ usd: 0, tokens: 0 }),
  fetch: async (q, opts) => [               // returns RawDoc[]
    { url, title, content, fetched_at }
  ]
};

Built-in:

  • web.js — Fetches via the user's available WebFetch tool through a stdin/stdout shim. Treats result as plain text/markdown.
  • arxiv.jshttps://export.arxiv.org/api/query (free, public, no key). Returns abstract + metadata.
  • github.jshttps://api.github.com/search/repositories + README pull (uses GH_TOKEN if set, otherwise unauthenticated rate limit).

Drop a new file in ~/.pro-workflow/fetchers/<name>.js to add a custom fetcher. Loaded at startup if present.

Budget enforcement

Pre-iteration: sum estimateCost across selected fetchers. If projected cumulative cost would exceed budget_usd, halt.

Post-iteration: track tokens used by the LLM compile step (Anthropic/OpenAI passthrough). Hard-kill on overrun.

Per-fetcher overrides via env: WIKI_LOOP_BUDGET_USD, WIKI_LOOP_MAX_PAGES, WIKI_LOOP_MAX_DEPTH.

Seed queue

SQLite-backed via wiki_seeds table:

fieldmeaning
querynatural-language seed
statuspendingactivedone|failed
parent_idseed that produced this one
depthBFS depth from root

Loop pops by (depth ASC, created_at ASC) so it explores breadth-first.

Convergence detection

After each compiled page, compute Jaccard overlap of claim-text tokens vs the prior 3 pages. If < 5 % novel content for 3 consecutive pages, halt and report converged.

Kill switch

touch ~/.pro-workflow/STOP

Loop checks per-iteration and halts gracefully. Remove file to resume next run.

Privacy guard

If wiki.config.md has private: true, the loop refuses any non-local fetcher and emits a warning. Only raw/ ingestion via manual seeds is allowed.

Reactive trigger (Phase 3.3.4)

scripts/file-watcher.js watches wiki/<slug>/wiki/**/*.md. On user-edited claim, enqueues a verification seed (verify: <claim>) at depth 0. Wired through pro-workflow's file-watcher.js hook.

Cron tick (Phase 3.3.4)

scripts/research-tick.js is launchable from any cron-style runner. Picks the oldest opted-in wiki with pending seeds and runs a single iteration. Hook event: pro-workflow:research-tick.

Output

Each run writes:

<wiki-root>/logs/research-<UTC-timestamp>.md   # human-readable run log
<wiki-root>/derived/run-<UTC-timestamp>.json   # structured stats

Run log lines:

[2026-05-08T10:42Z] seed-3 (depth=1) "memory consolidation in agents"
  fetcher=arxiv hits=3
  fetcher=web hits=2
  compiled wiki/concepts/memory-consolidation.md (claims=7, novel=4)
  enqueued 2 follow-up seeds
  cost so far: $0.04 / $0.50

Integration with wiki-query

Every compiled page goes through wiki-cli.js page so FTS5 stays consistent. The dedupe step calls searchWiki with the candidate claim text to find near-duplicates.

Status (Phase 3.3.1)

Ships: loop driver, seed queue, web/arxiv/github fetchers, budget caps, convergence detector, kill-switch, manual run command.

Defers:

  • Vector dedupe (Phase 3.3.2 via sqlite-vec)
  • LLM-judged claim novelty (current = Jaccard token overlap)
  • Cron + reactive (Phase 3.3.4)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wiki Research Loop AI skill do?

Auto-grow a pro-workflow wiki by running a budget-capped BFS research loop over pluggable source fetchers (web, arXiv, GitHub). Each iteration pops a seed from the queue, fetches sources, drafts a wiki page, dedupes claims against existing pages, enqueues follow-up seeds. Halts on budget cap, depth cap, or convergence. Use when the user says "research <topic>", "grow the <slug> wiki", "auto-research", or wants a knowledge base that builds itself overnight.

Why use Wiki Research Loop on TypingMind?

Because you install it once and use it with any model. Wiki Research Loop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wiki Research Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/wiki-research-loop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wiki Research Loop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wiki Research Loop?

As many as you like. As long as a model supports skills, you can use Wiki Research Loop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wiki Research Loop AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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