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AC mindgraph

Obsidian-style [[wikilink]] knowledge graph and learnable MindSkills for OpenClaw workspaces. Use for ALL of these: (1) Any workspace file read/write — always use [[wikilinks]] for people, projects, tools, concepts. (2) Running learned processes like knockout-test, seo-validator, competitor-analysis. (3) Querying knowledge — "what do I know about X", "show connections to Y". (4) Learning new repeatable processes — "learn a new mindskill called Z". (5) Memory maintenance — finding orphans, dead links, unconnected files. This skill is always active — treat [[wikilinks]] as standard practice in every markdown file you write.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 1 173 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Obsidian-style [[wikilink]] knowledge graph and learnable MindSkills for OpenClaw workspaces.

As a process C 53/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureObsidianData and analyticsMarketingSoftware developmenttype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
53/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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: 6. 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 53/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 29 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1173 tokens

    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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 629: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 29 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.