القائمة
Simulate-Deploy-Augment loop replacing traditional PDCA
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
MECE+ methodology for breaking problems into independently-solvable atomic tasks
Recursive sandboxing for 100+ iterations before human review
Patterns and utilities for building React components in house-maint-ai
| name | SDA Controller |
| description | Simulate-Deploy-Augment loop replacing traditional PDCA |
Replaces traditional PDCA (Plan-Do-Check-Act) with a faster, AI-native iteration loop.
| PDCA | SDA | Improvement |
|---|---|---|
| Plan | Simulate | Test before building |
| Do | Deploy | Canary first |
| Check | Augment | Real-time learning |
| Act | (Continuous) | No manual cycle |
┌─────────────────────────────────────────────┐
│ │
│ SIMULATE ──► DEPLOY ──► AUGMENT │
│ ▲ │ │
│ └───────────────────────┘ │
│ │
└─────────────────────────────────────────────┘
Run solution in high-fidelity sandbox before any real deployment.
simulate:
environment: sandbox
actions:
- Run test suite: `npm run test`
- Build check: `npm run build`
- Load test: `npm run load-test:smoke`
success_criteria:
tests_pass: true
build_success: true
performance_threshold: "p95 < 200ms"
Small-scale canary release with rollback capability.
deploy:
strategy: canary
initial_traffic: 5%
ramp_schedule:
- 5%: 10min
- 25%: 30min
- 50%: 1hr
- 100%: 2hr
rollback_trigger:
error_rate: "> 1%"
latency_p99: "> 500ms"
Agents learn from telemetry, adjusting in real-time.
augment:
telemetry_sources:
- Error logs
- Performance metrics
- User feedback signals
feedback_actions:
- Auto-fix common patterns
- Generate hotfix PRs
- Update configuration
learning_loop:
- Store successful patterns
- Avoid failed approaches
/iterate simulate # Run simulation only
/iterate deploy # Deploy with canary
/iterate full # Complete SDA cycle
/iterate rollback # Emergency rollback
| Component | Purpose |
|---|---|
eval-harness | Powers SIMULATE phase |
verification-loop | Continuous checks |
| GitHub Actions | DEPLOY automation |
| Sentry | AUGMENT telemetry |
| Metric | Target |
|---|---|
| Simulation pass rate | 100% |
| Canary success rate | > 99% |
| Mean time to augment | < 5min |
| Rollback frequency | < 1% |