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Agent Prompt Pack

Use these prompts during ADLC labs. Learners should adapt them, not paste blindly.

Inspect this repository. Do not edit files. Return:
- purpose
- tech stack
- start commands
- test commands
- main entry points
- important data flow
- likely risk areas
- unknowns that need manual verification
Trace the feature path for POST /tickets. Do not edit files. Show:
- HTTP route
- request parsing
- validation
- business logic
- database writes
- audit behavior
- tests
- failure modes
Use file paths.
Implement a bounded change:
Goal:
Add ticket priority with allowed values low, normal, high.
Constraints:
- Touch only the starter project.
- Preserve existing API behavior.
- Validate priority server-side.
- Default to normal.
- Add tests.
- Update API contract.
Verification:
Run python3 -m unittest -v and report results.
The bug is: non-agent users can approve drafts when they should receive 403.
Do not suggest a fix yet. Create a debugging plan with reproduction steps,
boundary checks, expected logs, and the test that should fail before the fix.
Review this diff for bugs, missing tests, security issues, data correctness,
API contract mismatches, and operational risks. Findings first. Reference files
and lines. Do not summarize unless there are findings or residual risks.
Review this runbook. Find missing start/stop steps, config assumptions, health
checks, logs, smoke tests, rollback steps, and common failure recovery paths.
Return concrete gaps only.
Review this capstone as if it must run for a real user tomorrow. Find gaps in
workflow fit, architecture, data, API, tests, operations, privacy, audit,
rollback, and agentic verification. Rank by severity.

Before sending a prompt:

  • Is the goal specific?
  • Is context included?
  • Are editable files bounded?
  • Is out-of-scope explicit?
  • Are verification commands included?
  • Is expected response format clear?
  • Did you ask for evidence?