engineering-mission-playbook
SolidUse when planning or replanning engineering missions that create, change, port, migrate, integrate, or preserve durable codebase behavior across UI, API, CLI, background jobs, data/migrations, libraries, or operator workflows. Defines investigation, scope inventory, coherent VAL-* contracts, evidence floors, multi-target task topology, engineering validation, root-cause patching, and durable guidance. For pure metric search, use optimization-mission-playbook instead.
Install
Quality Score: 86/100
Skill Content
Details
- Author
- Intelligent-Internet
- Repository
- Intelligent-Internet/zenith
- Created
- 3 months ago
- Last Updated
- 2 weeks ago
- Language
- Python
- License
- Apache-2.0
Similar Skills
Semantically similar based on skill content — not just same category
optimization-mission-playbook
Domain playbook for optimization missions — any task whose goal is to move a metric: performance, latency, throughput, memory, cost, score, quality, compression, ranking, solver, model/eval, and similar metric-improvement work. Defines how to think about and run an optimization mission: establishing the ground truth, trusting the measurement, profiling to the dominant cost, estimating the ceiling, generating and pruning disposable hypotheses, exploring cheaply before verifying expensively, guarding against metric gaming and overfitting, reasoning about correctness and trade-offs, and stopping on evidence.
engineering-mode
Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.
engineering-standards
The senior/staff-level engineering baseline every Fleetmind agent operates by — rigor, verify-before-claim, no assumptions, minimal faithful changes, test-first, security & PHI awareness, honest reporting, and a clear definition of done. Preloaded by all crew agents; also useful whenever high-quality engineering discipline is needed.