BC obsidian-wiki
Build and maintain a personal knowledge Wiki using the LLM Wiki pattern with OpenClaw-optimized step-by-step execution. Use sub-agents for parallel processing of file operations, content extraction, and wiki maintenance. This skill is designed for OpenClaw environments where file reading may be partial - it uses chunked reading and task delegation to ensure completeness. For PDF/Word conversion, it automatically checks and installs Python and required packages (pypdf, python-docx) if not present.
As a process C 63/100 · Has gaps — weak spots: when it triggers, consistency, running it twice
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5753 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 63/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 19 mutating operations with no state check
- 40Consistency. Frontmatter name (obsidian-wiki) differs from the folder (obsidian-wiki-auto)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5753 tokens
- 85Steps. 112 steps, 3 vague phrases
- 100Failures and branches. 4 branches, has a failure section
- 100Progress reporting. Reports progress
- low 11 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 501: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 112 items
- +3Output format is stated explicitly
- +4Has examples (20 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.