BC ontology-clawra
ontology-clawra v5.0 - Local Ontology Reasoning Engine A local structured reasoning engine with confidence management and automatic learning. Reads user workspace memory files for context. No cross-skill scheduling or network communication. Core Capabilities: - Ontology knowledge graph storage and reasoning - Confidence calculation and meta-cognition - Local memory search and rule learning File Access: Read/write only ~/.openclaw/skills/ontology-clawra/memory/ for ontology files; Read only ~/.openclaw/workspace/memory/*.md for user context. No cross-skill scheduling, no automatic network requests.
ontology-clawra v5.0 - Local Ontology Reasoning Engine A local structured reasoning engine with confidence management and automatic learning. Reads user…
As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 54/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (git) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 920 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)
- -37 of 8 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 607: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 13 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.