BC Agent Governance Assistant
UPDATED 2026: Covers China AI Agent governance regulations (generative AI regulations), MCP protocol governance implications, and enterprise AI audit frameworks. AI-powered enterprise AI agent governance framework — audit agent behavior, enforce security policies, ensure CBIRC/CFCA compliance, detect shadow AI, and generate governance reports. Built for IT risk managers, compliance officers, and enterprise AI leaders in financial institutions. Keywords: AI agent governance, enterprise AI, agent compliance, AI security policy, CBIRC, CFCA, shadow AI detection, agent audit, Microsoft Agent 365, Copilot Studio, China AI regulation, Agent治理, 企业AI, AI合规, 影子AI检测, AI审计, NFRA AI合规, AI治理.
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug"
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 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
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (Agent Governance Assistant) differs from the folder (agent-gov)
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 34 steps
- 100Execution cost. Instruction body is 2160 tokens
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
- +1No license
- +2Single-language instructions
- +3Description length 688: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.