CC wiki-query
Fast, read-only, index-first queries over an llm-wiki with bounded file reads, exact citations, and honest evidence gaps.
Promises fast searches over a local wiki with exact source citations and honest gaps in evidence. In reality: one instruction file at 658 tokens, no scripts, no model runs. Code quality scores 68, process scores 56. No critical issues found, but no way to verify it works—no sandbox, no execution examples.
For wiki lookups in an agent context, this might help if you already have an index and need query guidance. Install if you're prepared to adapt it locally.
Fast, read-only, index-first queries over an llm-wiki with bounded file reads, exact citations, and honest evidence gaps.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 1. 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 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (wiki-query) differs from the folder (query-lite)
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Execution cost. Instruction body is 658 tokens
- low The response is described with custom markup (4 tags): a typed call is more reliable
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 121: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 21 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.