| name | llm-governance |
| description | LLM content governance and compliance standards. Use when llm governance guidance is required. |
| allowed-tools | ["Bash(python3 skills/llm-governance/scripts/tool_checker.py *)","Bash(python3 skills/llm-governance/scripts/validator.py *)","Read","Write","Edit"] |
Purpose
Enforce LLM content governance for all LLM-facing files using TERSE mode defaults, rule-driven validation, and deterministic tooling.
Apply rules from skills/llm-governance/rules/99-llm-prompt-writing-rules.md and related governance rule files through standardized validators instead of ad-hoc scripts. Provide operational health reporting, capability matrix generation, and structural compliance checking for agents, skills, commands, and rules.
IO Semantics
Input: LLM-facing markdown and configuration files.
Output: Governance findings, severity classifications, suggested edits, and updated files when explicitly approved by higher-level commands.
Side effects: Backups created by orchestration commands before modifications; no direct writes required when running validation only.
Deterministic Steps
1. Toolchain Validation
- Use
tool_checker.py to confirm availability of required tools and select fallbacks.
- Abort governance execution for this skill when critical tools are missing and cannot be replaced safely.
2. Target Selection
- Select LLM-facing files using directory classification:
commands/**/*.md
skills/**/SKILL.md
agents/**/AGENT.md
rules/**/*.md
CLAUDE.md
AGENTS.md
.claude/settings.json
- Exclude non LLM-facing directories such as documentation, examples, tests, IDE metadata, and backup directories.
3. Automated Validation
- Run
python3 skills/llm-governance/scripts/validator.py <directory> across the selected scope.
- Validator uses
skills/llm-governance/scripts/config.yaml as Single Source of Truth (SSOT) for all validation rules.
- For each file, detect:
- Body bold markers outside code blocks.
- Emoji and decorative Unicode characters.
- Narrative paragraphs and conversational patterns.
- Missing or malformed frontmatter for skills, agents, commands, rules, and memory files.
- Classify violations by severity using rule definitions from
skills/llm-governance/rules/99-llm-prompt-writing-rules.md.
4. Content Normalization Guidelines
- Enforce TERSE mode:
- Rewrite narrative paragraphs into imperative directives.
- Remove conversational fillers and hedging language.
- Maintain high information density and precise terminology.
- Enforce formatting rules:
- Remove non code bold markers from body content.
- Remove emoji and non-essential decorative Unicode characters.
- Preserve code blocks and required technical symbols.
- Enforce structural rules:
- Ensure required frontmatter fields and section ordering per directory classification.
- Normalize heading levels and list formatting for clarity and determinism.
5. Operational Health and Matrix Reporting
- Generate agent and skill capability matrices using
agent-matrix.sh and skill-matrix.sh to snapshot capability-level, loop-style, and style coverage.
- Run
structure-check.sh to validate taxonomy-rfc compliance (layer: execution annotations, absence of legacy COMMAND.md files).
- Correlate governance findings with operational metadata for health reports and rollback candidate identification.
6. Integration with Orchestration Commands
- Delegate bulk analysis, candidate generation, backup creation, and writeback decisions to
/llm-governance and agent:llm-governance.
- Use this skill to interpret validator results, derive rewrite strategies, and keep governance behavior aligned with rule files.
Validation Criteria
- No body bold markers outside code blocks in LLM-facing files.
- No emoji or decorative Unicode characters in governed content.
- Communication is terse, directive, and TERSE-mode compliant.
- Required frontmatter fields and sections are present for each governed directory classification.
- All governance violations are either resolved or documented with justification in governance reports.