AC narrative-focus
Narrative Focus — detect and fix "narrative weight misalignment" in technical tutorials and interview prep articles. Trigger when users ask to review technical articles for concept weight misalignment, fix narrative focus, or label technical details by role during research/collection to prevent misalignment. Also triggers on: "检测叙述重心", "叙述重心错位", "概念权重", "角色标注", "按叙述重心规范收集", "审稿重心", "narrative focus", "narrative weight", "concept weight", "review narrative".
Narrative Focus — detect and fix "narrative weight misalignment" in technical tutorials and interview prep articles.
As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 7. 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
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 60Steps. 10 steps, 4 vague phrases
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1305 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)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 461: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 10 items
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.