Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
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Test your approach at extreme scales to find what breaks and what surprisingly survives.
Core principle: Extremes expose fundamental truths hidden at normal scales.
| Scale Dimension | Test At Extremes | What It Reveals |
|---|---|---|
| Volume | 1 item vs 1B items | Algorithmic complexity limits, index needs |
| Speed | Instant vs year-long | Async requirements, caching needs, timeouts |
| Users | 1 user vs 1B users | Concurrency issues, resource limits, auth bottlenecks |
| Duration | Milliseconds vs years | Memory leaks, state growth, data rot |
| Failure rate | Never fails vs always fails | Error handling adequacy, retry logic |
| Data size | 1 byte vs 1TB | Storage strategy, streaming vs buffering |
Normal scale: "Handle errors when they occur" works fine At 1B scale: Error volume overwhelms logging, crashes system Reveals: Need to make errors impossible (type systems, contracts) or expect them (chaos engineering, circuit breakers)
Normal scale: Direct function calls work At global scale: Network latency makes synchronous calls unusable Reveals: Async/messaging becomes survival requirement, not optimization
Normal duration: Works for hours/days At years: Memory grows unbounded, eventual crash Reveals: Need persistence or periodic cleanup — cannot rely on process memory
Normal load: One writer, no contention At 10k concurrent writes: Deadlocks, lock contention, queue buildup Reveals: Need optimistic locking, write batching, or event sourcing
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Build robust backend systems — APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), databases, performance optimization, security (OWASP Top 10), scalability patterns (microservices, caching, sharding), and testing. Use when designing APIs, implementing auth, optimizing queries, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
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Terse output mode — strips filler/articles/hedging, ~75% token reduction. Activate whenever: context is filling up, user says be brief/terse/caveman/less tokens, or CAVEMAN_TRIGGERED appears in context. Persists every response until CAVEMAN_RELEASED or user says stop/normal mode.
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Explore and debate solutions before writing code. Use when the user poses a design question, asks "how should I approach X", wants to think through options before committing, or says anything like "let's brainstorm", "what's the best way to...", "I'm not sure how to tackle this", or "should I use X or Y". Always runs before /ck:plan for novel or ambiguous features. No code is written — only: explore → clarify → spec → report.
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Implement a feature phase by phase from a phased JSON master or Markdown plan. Before each phase, confirm whether to create/run unit tests and whether to run ck:quality, unless explicit flags supply those choices. Supports token-budgeted execution, resumable state, and TDD handoff to ck:test.
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Fix a bug using Scout → Diagnose → Fix → Review → Finalize. Use when the user pastes an error message, stack trace, or test failure, or says "fix this bug", "something's broken", "tests are failing", "I'm getting an error". Also accepts `--from-quality <report>` or `--from-test <report>` to fix exactly the findings/failures a report lists. Modes (pick one): --fast (trivial errors — lint, type, build — skip scout and review), --hard (mandatory review, no auto-approve).
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Plan a feature or system before implementation. Use when the user says "plan this", "I want to build X", "how do I implement Y", or when /ck:brainstorm produces a spec.md. Always run before /ck:cook. Modes (pick one): --fast (simple, single-file), --hard (research + red-team + validate). Composable flag (combine with any mode): --tdd — propagates into the cook pipeline. Every phase gets Design Constraints and Quality/Testing State; ck:cook confirms optional test and quality checks per phase.
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