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Lint the wiki (Karpathy LLM-Wiki workflow)

obsidian_lint_wiki
Read-onlyIdempotent

Checks an Obsidian vault for orphans, broken links, stub pages, stale notes, and missing concept pages, returning actionable suggestions for cleanup.

Instructions

Comprehensive vault-hygiene check inspired by Karpathy's LLM-Wiki gist (gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). Returns five buckets of findings in one call: orphans (no inbound + no outbound), broken wikilinks, stub pages (under N words), stale pages (frontmatter last_reviewed or mtime older than M days), and concept candidates (capitalised phrases mentioned by ≥ K notes that lack their own page). Each finding carries a path + suggestion shaped so the agent can fix via existing tools (validate_note_proposal → create_note / append_to_note / rename_note). Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
folderNoRestrict the lint to a subfolder (default: whole vault)
stub_word_thresholdNoNotes shorter than this are flagged as stubs (default 100)
stale_daysNoNotes not touched for this many days are flagged as stale (default 365)
concept_min_mentionsNoA capitalised phrase mentioned by ≥ N distinct notes without a page is a concept candidate (default 3)
max_per_bucketNoCap per finding bucket so the response stays bounded (default 50)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint. The description adds that it is 'Read-only' and details the five buckets and their structure (path + suggestion), providing behavioral context beyond annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficient and well-structured: opens with purpose, lists buckets concisely, explains output format, and ends with 'Read-only'. Every sentence adds value with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully explains the return format: five buckets each with path and suggestion. It also explains the heuristics for stubs, stale pages, and concept candidates. References to sibling tools for fixing complete the picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All five parameters have good descriptions in the input schema (100% coverage). The description adds some context (e.g., how 'stub_word_threshold' relates to 'under N words') but does not significantly enhance beyond schema defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Comprehensive vault-hygiene check' and lists five specific finding buckets (orphans, broken wikilinks, stubs, stale pages, concept candidates). This distinguishes it from sibling tools like obsidian_stale_notes which only handle one bucket.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when performing a full audit before fixing, mentioning that findings are 'shaped so the agent can fix via existing tools.' It does not explicitly state when not to use it or list alternatives, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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