BD industry-research-report
生成专业行业研究报告，适用于投资尽调、并购评估等场景。当用户提到"研究某行业"、"行业分析"、"尽调报告"、"投资研究"、"行业深度报告"时加载本技能。采用经典四维分析框架：PEST宏观环境分析 + 波特五力竞争分析 + 价值链分析 + 市场规模测算（TAM/SAM/SOM），最终输出正式PDF报告。
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
ReferenceData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 49/100
- 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (industry-research-report) differs from the folder (industry-research-report-skill)
- 100Tools and files. No external tools needed
- 100Steps. 71 steps
- 100Execution cost. Instruction body is 779 tokens
- 100Running it twice. No mutating operations
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 151: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 71 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.
External checks
ClawHub: clean
This skill is a transparent industry research report workflow that uses public web searches and PDF generation without requesting credentials, local data access, or persistence.
LLM: benign (high) · VirusTotal: · 29 May 2026