AC competitor-analysis
Competitive analysis skill based on Zhang Zaiwang's methodology. Provides systematic competitive analysis framework, tools and templates to help users conduct professional competitive analysis.
Competitive analysis skill based on Zhang Zaiwang's methodology.
As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
AnalyzerData and analyticsMarketingResearchtype and topics are labelled automatically from the skill text
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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected scalar at node end at line 7, column 32: author: "Zhang Zaiwang" capers@qq.com ^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 52/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (competitor-analysis) differs from the folder (effective-competitive-analysis)
- 60Steps. 228 steps, 4 vague phrases
- 70Execution cost. Instruction body is 4688 tokens
- 100Tools and files. No external tools needed
- 100Result and completion. Output format and completion criterion are stated
- low 10 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)
- -219 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 193: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 228 items
- +3Output format is stated explicitly
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.