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AC wikicraft

Bootstrap and maintain a personal LLM Wiki — a persistent, compounding knowledge base of interlinked markdown pages that the LLM writes and maintains. Use when the user wants to build a knowledge base from scratch, ingest a new source into an existing wiki, query accumulated knowledge, or audit the wiki for gaps and contradictions. Triggers: "start a wiki", "build a knowledge base", "ingest this", "add to wiki", "what does the wiki say", "lint the wiki", "audit the wiki", "summarize this for the wiki".

ClawHub Agent Skills author: Bruno v1.0.0 MIT-0 3 files body ≈ 1 552 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Bootstrap and maintain a personal LLM Wiki — a persistent, compounding knowledge base of interlinked markdown pages that the LLM writes and maintains.

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerObsidianAI and agentsWriting and documentstype 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
93/100
safety, quality, tests
Safety 60%
95
Quality 40%
90
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Read Write Edit Bash

    Files scanned: 3. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 9 mutating operations with no state check
    • 40Consistency. Frontmatter name (wikicraft) differs from the folder (wiki-craft)
    • 55Failures and branches. 1 branches
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 43 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 1552 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 507: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (4 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This skill transparently helps an agent create and maintain a local markdown wiki, with no evidence of hidden code, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026