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CC wiki-manager

Manage LLM-compiled wikis: initialize, ingest/import, compile/query, research/audit, collect catalogs, track inventory/datasets/Ideas, review Idea/Project portfolios, manage archives/sessions/feedback, and generate outputs. Activates for /wiki commands; wiki or knowledge-base work; ingest, collect, catalog, compile, query, audit, librarian, inventory, idea, portfolio, business ideas, projects, datasets, archives, sessions, feedback, checkpoints, private adapters, adapter routing, explicit wiki skill-factory requests, personal specialist skills, expert review, specialist selection, provenance, external resource actions (including requests to read, review, or edit hosted documents), or questions in a directory with .wiki/ or a configured hub.

The skillemall take

Promises to manage AI-compiled wikis: initialization, data ingestion, query compilation, audits, idea catalogs. Triggers on /wiki commands and knowledge-base requests. The folder contains 24 files, but broken references and missing files are present. Quality score sits at 52/100—half. No critical errors found, yet this doesn't guarantee practical functionality. The skill hasn't run on any model. Body text is 6865 tokens—decent volume, but without a real test, it's unclear whether instructions work for wiki compilation or portfolio audits.

Setup carries risk. Broken files aren't cosmetic; they're potential failure points mid-workflow. Need a quick knowledge base? Look elsewhere. Willing to patch as you go? Try it, but set expectations low.

nvk/llm-wiki Agent Skills author: nvk 24 files body ≈ 6 865 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Manage LLM-compiled wikis: initialize, ingest/import, compile/query, research/audit, collect catalogs, track inventory/datasets/Ideas, review Idea/Project…

As a process C 63/100 · Has gaps — References files that are not bundled: path, ../category/slug.md, url

ProcedureObsidianSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
52
Run on models
none yet
Process rating
C
63/100
Has gaps
References files that are not bundled: path, ../category/slug.md, url
Tools and files w 18
0
Inputs and preconditions w 11
0
When it triggers w 12
70
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 24. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6865 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: path
  • warning missing-ref reference to a missing file: ../category/slug.md
  • warning missing-ref reference to a missing file: url
  • note frontmatter-key unknown frontmatter key "tools"
  • note edit-residue the text marks something as outdated (lines 8, 162, 194, 220, 339): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 63/100

Will not run. References files that are not bundled: path, ../category/slug.md, url
  • 0Tools and files. 3 referenced file(s) missing: path, ../category/slug.md, url
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 6865 tokens
  • 85Steps. 62 steps, 1 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (20 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 750: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 62 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (20 of 23)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.