Real Skill packageSource verifiedClawHub registry

证据化通用标引

用MCP证据构建并执行可复核标签体系

Identity and source

Publisher attributionyuanzhian-patsnapregistry owner unverified by skillvetai
Functional categoryAgent Engineering, Security & Governanceautomatically inferred · 53% rule confidence
Package forminstruction with code23 recorded files
Canonical sourceClawHub registryclawhub:yuanzhian-patsnap:evidence-based-labeling
Open canonical source ↗

Platform declarations

These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.

OpenClawnative officialProvenance: registry distribution

Independent structural checks

These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.

Claude Codepasses structure
Checker 0.1.0 · agent-skills-2026-08-13+claude-code-docs-2026-08-13 · 8/13/2026.claude/skills/evidence-based-labeling

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenAI Codexpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+codex-docs-2026-08-13 · 8/13/2026.agents/skills/evidence-based-labeling

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenClawpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+openclaw-docs-2026-08-13 · 8/13/2026skills/evidence-based-labeling

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

Installation and inspection

This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.

clawhub install @yuanzhian-patsnap/evidence-based-labeling
clawhub inspect @yuanzhian-patsnap/evidence-based-labeling --version 1.0.0

Security evidence

SkillVetAI static result: no findings detected

This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.

Status
completed
Coverage
full text content
Files
23 / 23 inspected as text
Checked
8/13/2026, 7:30:35 PM
Scanner
0.1.3
Policy
1.0.3
3 inferred permission indicators
  • network access — automatically inferred
  • filesystem read — automatically inferred
  • filesystem write — automatically inferred
3 dependency and API indicators
  • api: clawhub.ai
  • api: open.patsnap.com
  • api: open.zhihuiya.com
External clawhub result: clean

This is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.

Evidence checked
8/13/2026, 9:34:18 AM
Release binding
Matches this record
  • vt: clean
  • skillspector: clean
  • llm: clean

Recorded files

The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.

Package content hashsha256:63ef61ad1b280ee02534cce7a3267136d8fcf72353fc541cffe7669ebbfe5ebf
Show up to 23 recorded paths
  • agents/openai.yaml
  • assets/decision-rules-template.yaml
  • assets/label-confirmation-card-template.md
  • assets/task-config-template.yaml
  • assets/taxonomy-template.csv
  • README.md
  • references/default-decision-rules.md
  • references/domain-milk-protein-examples.csv
  • references/domain-milk-protein-rules.md
  • references/domain-milk-protein-taxonomy.csv
  • references/domain-milk-protein.yaml
  • references/input-output-contract.md
  • references/quality-and-review.md
  • references/taxonomy-design.md
  • references/workflow-modes.md
  • references/zhihuiya-mcp-orchestration.md
  • scripts/create_labeling_workbook.mjs
  • scripts/inspect_labeling_input.mjs
  • scripts/validate_labeling_output.mjs
  • scripts/validate_task_config.py
  • scripts/validate_taxonomy.py
  • skill-card.md
  • SKILL.md

Source changelog

- Initial release of the evidence-based-labeling skill focused on building, optimizing, and applying a structured labeling system for patents, scientific literature, technical documents, and more. - Introduces end-to-end, evidence-driven labeling workflows supporting open, semi-open, and closed taxonomy modes with clear rules and user-driven checkpoints. - Integrates PatSnap MCP for concept discovery, evidence gathering, and sample enhancement, with clear separation between model judgment, external evidence, and business decisions. - Provides tooling for validation of labeling inputs, taxonomy structure, task configuration, and output quality, with auxiliary scripts included. - Emphasizes clear output statuses, role-based review queues, and quality tracking, with strong safeguards for data integrity, configuration, and MCP usage.