Agent skill

certainty-grade

Apply HIGH/MEDIUM/LOW certainty grading to all agent findings and recommendations. Use to gate human review, auto-fix, or autonomous action.

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Install this agent skill to your Project

npx add-skill https://github.com/elasticdotventures/_b00t_/tree/main/plugins/next-task/skills/certainty-grade

SKILL.md

Certainty Grade Framework

All agent findings MUST include a certainty grade. Grade determines action path.

Grades

Grade Basis Action
HIGH Deterministic: regex match, static check, type error, compiler output Auto-fix permitted
MEDIUM Heuristic: code ratio analysis, pattern frequency, structural smell Flag for review
LOW AI judgment: semantic meaning, intent, contextual inference Human gate required

Steps

  1. Identify the finding type. # output: finding_type (deterministic|heuristic|semantic)
  2. Assign grade:
    • HIGH if finding_type == deterministic (provable without AI)
    • MEDIUM if finding_type == heuristic (pattern-based, probabilistic)
    • LOW if finding_type == semantic (requires AI reasoning)

    output: certainty (HIGH|MEDIUM|LOW)

  3. Attach grade to finding in output. Format: [HIGH] <finding>, [MEDIUM] <finding>, [LOW] <finding>
  4. For batched findings, group by certainty. # output: grouped_findings

Action Gates

HIGH  → auto-apply fix if safe, log result
MEDIUM → surface to user, suggest fix, await confirmation
LOW   → block action, require explicit human approval, explain reasoning

Output Format

[HIGH]   console.log() at src/foo.ts:42 — remove (debug artifact)
[MEDIUM] function doThing() lacks error handling — review
[LOW]    variable naming may be misleading given domain context — human review

Integration

Used by: /next-task, /code-quality, all agents that produce findings. b00t MUST grade all MCP tool outputs before acting on them.

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