SKILLEMALL.ai
GitHub ↗ RU Sign in

BC competitive-landscape-zhcn

技术领域竞争格局分析，包含玩家分层、技术路线差异化与空白机会识别。当用户请求玩家图谱绘制、行业竞争对手分析、技术路线玩家对比、国内外玩家对比，或在特定技术领域内开展多玩家竞争评估时使用。

ClawHub Claude Code author: yuanzhian-patsnap v1.0.1 MIT-0 18 files body ≈ 1 595 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

技术领域竞争格局分析，包含玩家分层、技术路线差异化与空白机会识别。当用户请求玩家图谱绘制、行业竞争对手分析、技术路线玩家对比、国内外玩家对比，或在特定技术领域内开展多玩家竞争评估时使用。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "copyright"
  • note frontmatter-key unknown frontmatter key "provider"
  • note frontmatter-key unknown frontmatter key "deliverable-default"
  • note frontmatter-key unknown frontmatter key "fallback-policy"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 130 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1595 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Description length 93: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 130 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.

External checks

ClawHub: clean
This is a documentation-only research skill for producing competitive landscape reports, with expected local output files and public/source-based research steps.
LLM: benign (high) · VirusTotal: · 13 Aug 2026