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AC wiki-knowledge-base

LLM-powered personal wiki knowledge base system. Use this when user wants to build and maintain a persistent wiki using LLMs following the LLM Wiki pattern (karpathy/442a6bf555914893e9891c11519de94f). This system implements: wiki initialization (two-layer architecture - wiki content and schema), source ingestion with cross-reference maintenance, querying with synthesis and citations, health checking (lint), and schema management. Source files are stored externally (e.g., in project's raw/ folder), not copied into the wiki. Perfect for: personal knowledge management, research wikis, reading companions, team knowledge bases. Triggers on: wiki knowledge base, LLM wiki, personal wiki, build wiki, knowledge management, or any mention of organizing accumulated knowledge with an LLM.

ClawHub Agent Skills author: 1knownothing v1.0.0 MIT-0 25 files body ≈ 786 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ReferenceAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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: 20. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (wiki-knowledge-base) differs from the folder (llm-wiki-skills)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 35 steps
    • 100Execution cost. Instruction body is 786 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 787: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a markdown-only local wiki skill that openly reads and updates wiki files for knowledge management, with no evidence of hidden code, credential use, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026