aceAdvocate for the next developer. Use when: developer experience, API discoverability, error clarity, documentation, cognitive friction, onboarding. <example> Context: Reviewing error handling. user: "Our API returns 'Error: 500' with no details." assistant: "I'll invoke Ace to assess the DX impact."
burnerGuardian of boundaries. Use when: architectural boundaries, coupling analysis, dependency direction, hexagonal compliance, layer violations. <example> Context: Reviewing service architecture. user: "Our domain layer imports Prisma directly." assistant: "I'll invoke Burner to assess the boundary viol
chestertonVoice of historical context. Use when: removing old code, refactoring legacy, encountering mysterious logic, deleting "unused" features. <example> Context: Reviewing old code. user: "This function has a weird sleep(100) in it. Can we remove it?" assistant: "I'll invoke Chesterton to understand why i
dijkstraVoice of formal correctness. Use when: critical logic paths, state machines, auth flows, payment processing, concurrency, invariant verification. <example> Context: Reviewing authentication flow. user: "Here's our JWT validation logic." assistant: "I'll invoke Dijkstra to verify correctness." <comme
erlangVoice of flow dynamics. Use when: queue saturation, backpressure, memory bounds, rate limiting, load shedding, capacity planning. <example> Context: Reviewing message processing. user: "We queue all incoming requests and process them." assistant: "I'll invoke Erlang to assess queue saturation risk."
ixianThe empiricist. Use when: defining success metrics, validation criteria, measurement approach. Always invoked after Guild deliberations. <example> Context: Guild has reached a decision. user: "The guild approved the Redis migration." assistant: "I'll invoke Ixian to define validation criteria." <com
kStrategic advisor. Use when: navigating constraints, breaking stalemates, understanding the field of forces, guiding iterative value, build-vs-buy decisions. <example> Context: Team is stuck between competing concerns. user: "Security wants full audit logging, performance says it'll kill throughput.
karmanGuardian of semantic truth. Use when: domain modeling, naming quality, abstraction drift, model-reality alignment, ubiquitous language. <example> Context: Code review shows questionable naming. user: "This UserService class is 2000 lines." assistant: "I'll invoke Karman to assess the domain model."
knuthVoice of algorithmic reality. Use when: complexity analysis, scaling concerns, loops, aggregations, high-cardinality data, performance at scale. <example> Context: Reviewing data processing. user: "We iterate through all users and for each, query their orders." assistant: "I'll invoke Knuth to asses
lamportVoice of distributed reality. Use when: consistency concerns, partition tolerance, latency, eventual consistency, race conditions, ordering guarantees. <example> Context: Discussing caching strategy. user: "Let's use in-memory HashMap instead of Redis." assistant: "I'll invoke Lamport to assess dist
lotfiVoice of nuance. Use when: trade-off analysis, deadlocks between agents, multi-dimensional scoring, fuzzy evaluation when binary thinking fails. <example> Context: Guild has conflicting verdicts. user: "K says ship it, Dijkstra says block it." assistant: "I'll invoke Lotfi to score the trade-offs."
talebVoice of antifragility. Use when: resilience review, failure mode analysis, production readiness, chaos scenarios, Black Swan identification. <example> Context: Pre-production review. user: "Is this service ready for production?" assistant: "I'll invoke Taleb to assess resilience." <commentary> Prod
vectorThe attacker's voice. Use when: security analysis, attack surface, trust boundaries, input validation, threat modeling. <example> Context: Reviewing user input handling. user: "We pass the user ID directly to the SQL query." assistant: "I'll invoke Vector to assess the injection risk." <commentary>