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Llm wiki choose

Skill po4yka/llm-wiki-skills/skills/llm-wiki-choose

Portable Agent Skills for building, operating, evaluating, and governing LLM-Wiki knowledge systems.

Install
npx -y skills add po4yka/llm-wiki-skills --skill llm-wiki-choose

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What its author says it does

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Help a user choose between ready-made LLM-Wiki tools, a local-first workflow, a team rollout, retrieval/GraphRAG systems, or a custom implementation. Use when the user asks what solution fits their needs, whether to use OpenWiki/Obsidian/qmd/GraphRAG, or whether to build their own.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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LLM-Wiki Choose

Goal

Interview the user enough to recommend a practical path: adopt a ready-made solution, assemble a local-first workflow, roll out a team process, or build a custom LLM-Wiki.

When to use

  • The user asks which LLM-Wiki approach fits: a ready-made tool, a local-first git workflow, a team rollout, a retrieval/GraphRAG system, or a custom build.
  • The user explicitly asks whether OpenWiki, Obsidian, qmd, or a GraphRAG system suits their case.
  • The user is comparing infrastructure options before committing to an architecture or before starting llm-wiki-paf-adoption.
  • The user is still skeptical whether an LLM-Wiki is worth building at all.

Inputs

  • User type (individual, team, company, product builder) and corpus type/scale.
  • Privacy tier (public, internal, sensitive, regulated) and update/freshness needs.
  • Preferred coding agent and existing tooling (Obsidian, GitHub, qmd, vector DB, GraphRAG, OpenWiki, custom scripts).
  • Write tolerance (read-only advisory, PR-based writes, direct writes) and infrastructure/maintenance budget.

Procedure

1. Gather requirements

Ask only the questions that materially change the recommendation. Cover:

  • user type: individual, team, company, product builder;
  • corpus: code repo, research papers, docs folder, Obsidian vault, chat exports, web clips, PDFs, media transcripts;
  • scale: rough number of sources/pages;
  • update frequency and freshness needs;
  • privacy: public, internal, sensitive, regulated;
  • preferred agent: Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, other;
  • local-first and git requirements;
  • write tolerance: read-only advisory, PR-based writes, direct writes;
  • existing tools: Obsidian, GitHub, qmd, vector DB, GraphRAG, OpenWiki, custom scripts;
  • budget for infrastructure and maintenance.

If the user already provided these facts, do not ask again.

2. Classify the case

Use this routing:

CaseDefault recommendation
Small personal vault, local-firstMarkdown + git + agent skills + rg; add qmd later.
Code repository docsOpenWiki-style repo wiki or custom docs-as-code workflow.
Existing Obsidian vaultRefactor in place with protected human sections and lint gates.
Team/company knowledgePR-based agent writes, CODEOWNERS, permissions, lint reports.
Large retrieval-heavy corpusHybrid search or graph/RAG system, with wiki as compiled surface if human review matters.
Product/plugin ideaCustom architecture with provenance, staged review and safe writes from day one.

3. Answer adoption objections before tool choice

If the user is still asking whether LLM-Wiki is worth it, invoke or summarize llm-wiki-faq before recommending tools.

Use the evidence hierarchy:

direct LLM-Wiki evidence -> adjacent RAG/memory/context evidence -> local pilot metrics

Do not claim that adjacent GraphRAG/memory evidence proves every LLM-Wiki workflow will work.

4. Decide whether Obsidian is needed

Obsidian is optional.

Recommend Obsidian when:

  • the user already uses it;
  • human reading/editing of Markdown is central;
  • backlinks, graph view and local note UX matter;
  • the workflow is personal or research-heavy.

Do not require Obsidian when:

  • the target is repository documentation;
  • the team already uses docs-as-code and pull requests;
  • the user wants minimal CLI/git workflow;
  • the user only needs agent-readable context.

5. Re-verify current external options

Before recommending a current tool, browse and cite:

  • official docs or repository README;
  • recent commits/releases/issues when maturity matters;
  • license;
  • local/cloud inference support;
  • output format and data portability;
  • provenance/review features;
  • install path and supported agents.

6. Score options

Score 1-5 across:

  • fit to use case;
  • local-first/data ownership;
  • setup effort;
  • maintenance burden;
  • provenance/trust controls;
  • agent compatibility;
  • scaling path;
  • reversibility/lock-in.

7. Recommend one path

Give a primary recommendation and one fallback. Avoid listing every possible tool as equal.

Use this output:

## Recommendation

## Why this fits

## Evidence level

## Alternatives considered

| Option | Fit | Setup effort | Trust controls | Notes |
|---|---:|---:|---:|---|

## Setup plan

## Risks and review gates

## Next skill to run

Decision rules

  • Prefer a thin local-first workflow before adding databases.
  • Prefer ready-made tools for repo documentation and demos.
  • Prefer custom implementation only when trust, provenance, scale, UX or integration needs justify it.
  • Prefer PR-based writes for teams.
  • Do not recommend current project maturity from memory; browse.
  • For skeptical users, recommend a 20-50 source pilot and llm-wiki-eval rather than a big migration.

Output

A short markdown report following the section 7 template: Recommendation, Why this fits, Evidence level, an Alternatives considered table (Option / Fit / Setup effort / Trust controls / Notes), Setup plan, Risks and review gates, and Next skill to run.

Safety gates

  • Stop before suggesting cloud ingestion for sensitive material unless the user explicitly accepts it.
  • Do not recommend direct-write agents for team knowledge without review gates.
  • Mark uncertain tool claims as verify-before-use.
  • Do not make Obsidian mandatory unless the user's workflow actually benefits from it.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.