Llm wiki choose
Portable Agent Skills for building, operating, evaluating, and governing LLM-Wiki knowledge systems.
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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.
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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:
| Case | Default recommendation |
|---|---|
| Small personal vault, local-first | Markdown + git + agent skills + rg; add qmd later. |
| Code repository docs | OpenWiki-style repo wiki or custom docs-as-code workflow. |
| Existing Obsidian vault | Refactor in place with protected human sections and lint gates. |
| Team/company knowledge | PR-based agent writes, CODEOWNERS, permissions, lint reports. |
| Large retrieval-heavy corpus | Hybrid search or graph/RAG system, with wiki as compiled surface if human review matters. |
| Product/plugin idea | Custom 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-evalrather 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.