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Mission - Agent Instructions as Executable Constraints

Goal

Turn prose instructions into machine-checkable rules across five categories and emit a rule report a reviewer can score.

Inputs

  • docs/agent-rules.md with one rule per heading, each carrying slug, category, description, and a check field
  • A demo agent run that intentionally violates two rules

Deliverables

  • Parser that loads agent-rules.md into a dataclass
  • rule_checker.py style functions, one per check referenced
  • rule_report.json with pass/fail per rule and an aggregate severity

Acceptance

  • python3 code/main.py exits zero
  • Output prints the parsed rule set, the run trace, and pass/fail per rule
  • rule_report.json catches the two intentional violations

Out of scope

  • Wiring the checker into CI. The lesson exits at a written report.
  • Framework guardrails (OpenAI SDK, LangGraph interrupts). The rule set is the human-readable contract those implement.

References

  • docs/en.md - full lesson
  • code/main.py - reference implementation
  • outputs/skill-rule-set-builder.md - extracted skill