AC skill-discovery
Search for reusable skills across OpenSpace's local registry and cloud community. Reusing proven skills saves tokens, improves reliability, and extends your capabilities beyond built-in tools.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 2. 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 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (skill-discovery) differs from the folder (openspace-skill-discovery)
- 100Tools and files. No external tools needed
- 100Steps. 6 steps
- 100Execution cost. Instruction body is 385 tokens
- 100Running it twice. No mutating operations
- 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 192: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 6 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.
External checks
ClawHub: suspicious
This skill is a small discovery helper, but it can automatically import community skills and then have the agent follow their instructions without a clear approval gate.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026