CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

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.

Quality

87%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

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

In a plugin session, call the providers MCP server's run_provider_task tool with task: "research" and the runner arguments below, for example args: ["run", "agent-memory", "--fetchers", "github"]. Its optional GitHub token comes from the plugin configuration dialog. The direct CLI uses PRO_WORKFLOW_GITHUB_TOKEN when explicitly supplied. Never retrieve tokens from existing machine credentials.

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:

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.js — https://export.arxiv.org/api/query (free, public, no key). Returns abstract + metadata.
  • github.js — https://api.github.com/search/repositories + README pull (uses the explicitly configured GitHub token, otherwise unauthenticated rate limit).

Standalone CLI runs load custom fetchers from ~/.pro-workflow/fetchers/<name>.js. The plugin MCP server uses only bundled fetchers so configured tokens are not handed to user-supplied modules.

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
statuspending → active → done|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)
Repository

rohitg00/pro-workflow

Path

skills/wiki-research-loop/SKILL.md

Last updated
First committed

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.