SKILLEMALL.ai
GitHub ↗ RU Sign in

BD zeelin-patent-retriever

Team ZeeLin’s production-grade patent evidence retrieval skill for Google Patents BigQuery. Converts natural-language research intent into auditable multi-round retrieval plans with explicit filters (keywords, country, date, assignee/inventor, IPC/CPC), and outputs validated JSON artifacts for downstream analysis and drafting. Triggers: patent search, prior art, google patents, bigquery patent, 专利检索, 专利查新, 技术情报.

ClawHub Agent Skills author: Yuwen Yang v0.1.2 13 files body ≈ 1 824 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGoogle CloudData and analyticsInfrastructuretype 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%
71
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 13. 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 "homepage"
  • note frontmatter-key unknown frontmatter key "emoji"

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
  • 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 (zeelin-patent-retriever) differs from the folder (patent-retriever-bigquery)
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 52 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Execution cost. Instruction body is 1824 tokens
  • 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)
  • +3Output format is not stated: the model decides each time
  • -31 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 415: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This patent-search skill mostly matches its purpose, but it needs review because it uses Google Cloud credentials and does not strictly enforce the promised Google Patents BigQuery table scope.
LLM: suspicious (high) · VirusTotal: · 29 May 2026