BC ai-identity-verification
AI 身份核验与溯源工具（三锚一票方法论）：对任意 AI 智能体/对话/Agent 执行身份核验——账号锚（Account Anchor 登录身份/可验证凭证）、行为锚（Behavior Anchor 行为模式一致性）、记忆锚（Memory Anchor 长期记忆一致性）三维交叉核验，最终一票给出可信度结论（Level 1-4）。对标国际标准：W3C DID v1.1 / Verifiable Credentials / did:trail / MCP-I / EU AI Act Art.50-52 / C2PA 内容溯源。适用：判断对话 AI 是不是同一个人/同一个 Agent、AI 生成内容溯源、Agent 身份审计、医疗 AI 身份核验（细分第一）。触发词：AI 身份、身份核验、三锚一票、这是同一个AI吗、AI 溯源、Agent 身份审计、verify AI identity、AI provenance。
AI 身份核验与溯源工具（三锚一票方法论）：对任意 AI 智能体/对话/Agent 执行身份核验——账号锚（Account Anchor 登录身份/可验证凭证）、行为锚（Behavior Anchor 行为模式一致性）、记忆锚（Memory Anchor…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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-long-hermesdescription is 411 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "description_en"
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. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 863 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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 411: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (1 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.