BF research-assistant
Conduct multi-source deep research and generate structured reports. Use when the user needs to: (1) investigate a company, industry, or topic from multiple angles, (2) cross-verify information across sources, (3) generate SWOT analysis, competitor analysis, or industry research reports, (4) compile a structured research brief with source citations.
Conduct multi-source deep research and generate structured reports.
As a process F 37/100 · Will not run — References files that are not bundled: URL
How to improve
- The text references files that are not there: add them or drop the references.
- 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
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Conduct multi-source deep research and generate structured reports… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
missing-refreference to a missing file: URL - note
frontmatter-keyunknown frontmatter key "emoji"
Process rating: all ten parameters 37/100
- 0Tools and files. 1 referenced file(s) missing: URL
- 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. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (research-assistant) differs from the folder (zcx-research-assistant)
- 100Steps. 23 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 1659 tokens
- low The response is described with custom markup (5 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
- +1No license
- +2Single-language instructions
- +3Description length 350: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.