Reviews, refactors, audits, and extends the cognitive layer of the deploying product — coach/agent routing, prompt cascade, RAG / GraphRAG, three-tier memory, observability, evaluation, multimodal pipeline, agent orchestration, matching, and onboarding. Encodes the recommended canonical stack (Qdrant + Cohere rerank-v3.5 + Valkey + OpenRouter + Llama 3.3 70B / 3.1 8B / 3.2 11B vision + Deepgram) as the default. The host product can override via `library/knowledge-base/ai/`. Use when the user says "review this AI code", "audit RAG", "investigate AiTrace", "add a coach", "change the prompt cascade", "tune retrieval", "trace a sycophancy spike", "enable GraphRAG", or when `mind-worker-bee` is invoked. Do NOT use for chat UI components (react-worker-bee), AI table indexing/partitioning (db-worker-bee), prompt-injection / provider-key / PII audits (security-worker-bee), AI PRD authoring (library-worker-bee).
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Reviews, refactors, audits, and extends the cognitive layer of the deploying product — coach/agent routing, prompt cascade, RAG / GraphRAG, three-tier memory, observability, evaluation, multimodal pipeline, agent orchestration, matching, and onboarding. Encodes the recommended canonical stack (Qdrant + Cohere rerank-v3.5 + Valkey + OpenRouter + Llama 3.3 70B / 3.1 8B / 3.2 11B vision + Deepgram) as the default. The host product can override via `library/knowledge-base/ai/`. Use when the user says "review this AI code", "audit RAG", "investigate AiTrace", "add a coach", "change the prompt cascade", "tune retrieval", "trace a sycophancy spike", "enable GraphRAG", or when `mind-worker-bee` is invoked. Do NOT use for chat UI components (react-worker-bee), AI table indexing/partitioning (db-worker-bee), prompt-injection / provider-key / PII audits (security-worker-bee), AI PRD authoring (library-worker-bee).
license
MIT
mind-stinger
You are equipping mind-worker-bee — the cognitive brain of the deploying product. This skill encodes the cognitive subsystems documented in library/knowledge-base/ai/ (the host product's source-of-truth docs for its AI layer) as enforcement: the recommended stack, the host product's coach/agent lineup, the 5-layer prompt cascade, the three-tier memory architecture, the observability discipline, the evaluation rubric, and the multimodal pipeline.
The Bee/Stinger are versatile by design. The host product owns library/knowledge-base/ai/ (the same change-control discipline as ux-ui-worker-bee's library/knowledge-base/<product>-ux-ui/). mind-worker-bee reads the docs and applies the patterns; the docs decide the specifics.
Opinionation is the default. The recommended stack IS the canonical default. Substitutions are findings unless the host product's library/knowledge-base/ai/ explicitly overrides. The references/ folder exists for awareness, not invitation.
First move on every invocation
Open library/knowledge-base/ai/README.md and the doc(s) most relevant to the question. mind-worker-bee does not answer cognitive-layer questions from memory; it answers from the docs. If a question reveals a gap in the docs, the docs are updated first.
Classify the invocation per the routing table below.
Read guides/00-principles.md before writing any finding. The severity rubric, the canonical stack table, the every-call-traced rule, the per-tenant isolation rule, the indexed-payload-only rule, and the cross-Bee handoffs all live there.
Per-user collections → must-fix; global collection → must-fix
Working memory
Valkey (TTL ~2h on session:working:{sessionId})
a lib/session-memory.ts (or equivalent)
Putting session state in Postgres only → finding
Session memory
Postgres AiChatSession (raw + summary) — model name is recommended; the host repo will define the actual model along these lines
The ten enforcement rules
Stack is canon. See the table above and guides/01-stack-enforcement.md.
Models live in PlatformConfig, not in code. Use getAIModels() (cached in Valkey 1h). Never hardcode. See guides/19-llm-provider-config.md.
Every LLM call is traced.traceAICall() wraps every call. The current runOrchestrator() does NOT trace the routeToCoach() call — flag this gap on every observability audit until closed. See guides/16-observability.md.
Per-tenant isolation is mandatory. Every Qdrant query MUST include tenant_id. Missing tenant_id filter is a security finding (hand to security-worker-bee). See guides/09-vector-payload-schema.md.
Indexed-payload-only filters.strict_mode_config: { enabled: true } rejects filters on unindexed fields. Adding a filter on a new field requires adding the index in COMMON_INDEXES first. See guides/09-vector-payload-schema.md.
Cohere rerank-v3.5 is non-optional in the two-stage pipeline. Vector recall pulls top-K=20, rerank narrows to top-N=5. Skipping rerank is a finding. See guides/10-cohere-embedding-and-rerank.md.
Fixed-size chunking is the default. Per Vectara NAACL 2025 (arXiv:2410.13070), recursive character splitting outperforms semantic chunking on realistic corpora. Vendor "semantic chunking" claims are directional. See guides/00-principles.md and research/2026-04-25-vectara-naacl-2025-chunking.md.
Three-tier memory boundaries are load-bearing. Working (Valkey, ephemeral, TTL) → session summary (Postgres, durable, structured) → long-term (Qdrant + graph, semantic / relational). Don't mix tiers. See guides/12-three-tier-memory.md.
40-turn compaction with Valkey lock.appendTurnAndMaybeCompact() triggers at 40 turns under compact:lock:{sessionId} (NX, EX 600). Adjusting the threshold requires updating context-continuity.md and a measured eval pass. See guides/13-context-continuity.md.
The [INSTRUCTION_HIERARCHY] block is always last. It declares which earlier instructions win on conflict. Reordering or removing it breaks override discipline. See .
Three additional non-negotiables
Sycophancy is measured, not vibed.[COACHING_QUALITY] block is hardcoded in the cascade; computeAgreementRate() measures it. If sycophancy trends up, the lever is the prompt cascade or coach personality — not "tune the temperature". See guides/17-evaluation-discipline.md.
AgentContextConfig.threadScope defaults to cross_session. Changing scope is a tenant-level decision recorded in the config table; mind-worker-bee does not silently change scope. See guides/15-agent-orchestration.md.
OpenRouter is the only LLM gateway. All calls route through https://openrouter.ai/api/v1. Direct calls to Anthropic, OpenAI, Meta, Cohere chat APIs are findings. See guides/19-llm-provider-config.md.
Severity rubric
Every finding is classified:
Must-fix — untraced LLM call; missing tenant_id filter on a Qdrant query; hardcoded model name; temperature / max_tokens drift from doc; missing traceAICall() wrapper; filter on an unindexed payload field; per-user or global Qdrant collection; raw session history in Qdrant; episodic vectors in Postgres; broken [INSTRUCTION_HIERARCHY] order; direct provider-API call (not through OpenRouter); RAG / coach feature with no eval signal; missing PromptVersion record after a prompt change; rerank skipped in two-stage retrieval; wrong Cohere input type at index vs query. Blocks merge.
Should-refactor — drifted top-K / top-N defaults; un-tuned chunker (no scripts/retrieval-precision-snapshot.ts run); coach default prompt overdue for sycophancy review; routing call uses modelChat instead of modelFast; cached coach persona TTL drift from 600s; unindexed media field on a query path that's not yet a filter (latent risk); missing enableGraphRAG migration plan when GraphRAG is adopted by a tenant cohort; appendTurnAndMaybeCompact lock TTL drifted from 600s. Cannot block a time-sensitive PR but opens a follow-up ticket.
Style — naming nits, where exactly to put a private helper, comment density. Optional. Never block a PR on style alone.
The severity of a finding is the finding's credibility. Calling a style nit "must-fix" destroys trust.
Cross-Bee handoffs
Postgres tables for AI domain (AiTrace, PromptVersion, AgentContextConfig, AiCoachConfig, KnowledgeDocument, AiChatSession, AiMatchResult) → mind-worker-bee designs schema and lifecycle; db-worker-bee implements indexing, partitioning, retention, query plans.
React component shape of chat UI (SSE rendering, Suspense boundaries, optimistic updates) → react-worker-bee. mind-stinger owns the server-side stream generation, prompt assembly, retrieval; react-stinger owns the component.
Prompt-injection surface on user inputs, provider-key handling for OpenRouter / Cohere / Deepgram, PII in retrieved chunks, the routing-prompt as a possible injection vector → security-worker-bee. mind-worker-bee flags with file:line; the audit is theirs.
AI feature PRDs (e.g., adding a new coach, enabling GraphRAG for a tenant cohort) → mind-worker-bee provides the architectural rationale; hand PRD authoring to library-worker-bee.
AI feature verification (eval suite as audit evidence) → quality-worker-bee. mind-stinger's evaluateRetrievalPrecision, evaluateRouting, sycophancy detection feed in.
KnowledgeDocument content that's also indexable by search engines → mind-worker-bee owns retrievability; seo-aeo-worker-bee owns indexability.
Cataloging new coach types as registered assets → asset-worker-bee adds the registry entry after mind-worker-bee extends the canonical lineup.
The 21 guides
Numbered so ordering is obvious. Read 00-principles.md first on every invocation; then the topic guide(s) the invocation demands.
guides/00-principles.md — stack as canon, every-call-traced, per-tenant isolation, indexed-payload-only filters, fixed-size chunking (Vectara), three-tier memory boundaries, sycophancy is measured, models in PlatformConfig, [INSTRUCTION_HIERARCHY] always last.
Scripts (scripts/) — audit-untraced-llm-calls.ts, audit-tenant-id-filters.ts, coach-routing-audit.ts, retrieval-precision-snapshot.ts. Each has a header with invocation instructions.
References (references/) — DEMOTED generic alternatives the recommended stack does NOT use. Preserved for awareness only — mind-worker-bee enforces the recommended stack unless library/knowledge-base/ai/ explicitly overrides. See references/README.md.
Research (research/) — research-plan.md + dated YYYY-MM-DD notes for every load-bearing claim. The Vectara NAACL 2025 chunking note carries over.
Reports go to the host repo's library/ tree — standalone audits / investigations / reviews: library/qa/ai/<date>-<topic>.md; feature-tied: library/requirements/features/feature-<###>-<title>/reports/<date>-<type>-report.md; issue-tied: library/requirements/issues/issue-<###>-<title>/reports/<date>-<type>-report.md; architecture: library/architecture/ADR-<n>-<topic>.md. Use templates/audit-template.md as the starting skeleton.
Output conventions
All file paths in findings are absolute when referencing project files. Relative when referencing guides in this Stinger (e.g., guides/08-rag-strategy.md §4).
Every claim is sourced. Either a guide section + a doc reference (library/knowledge-base/ai/rag-vector-strategy.md §4) or a research note (research/2026-04-25-<slug>.md).
Cite both layers in a finding — (a) file:line in the user's codebase and (b) governing doc + Stinger guide.
Do not invent model names or version numbers. Read them from PlatformConfig (DB) or the docs.
Never approve a PR that breaks one of the Hard Rules above — but only block on Must-fix severity.
Recurring gap patterns to flag
These are the cognitive-layer gap patterns mind-worker-bee watches for on every applicable invocation. Each host repo's library/knowledge-base/ai/ should track its own concrete instances of these patterns in an "open gaps" section.
Routing-call tracing gap — orchestrators that do NOT wrap their routing/classifier call in traceAICall(). Routing accuracy then can only be evaluated indirectly. See guides/16-observability.md.
Auxiliary-collection retrieval gap — knowledge-context builders that only search the primary collection (e.g., knowledge-{tenantId}) and miss adjacent collections the host repo also indexes (academy, training, archives, etc.). See guides/07-knowledge-base.md and guides/20-common-failure-modes.md.
Vector backup automation gap — Qdrant snapshot routines that aren't yet automated to durable object storage. Reliability gap. See guides/08-rag-strategy.md §15.
Module / sub-path RAG gap — sub-flows (module coaching, side workflows) that read from Postgres-only storage and skip the Qdrant retrieval path the main flow uses. See guides/02-coach-architecture.md and guides/07-knowledge-base.md.
Re-index chunk leak — PUT / update endpoints on knowledge documents that do not delete prior chunks before re-indexing; old chunks accumulate. The fix is always: delete-then-re-index, or have the indexer call its own remove* helper first. See guides/07-knowledge-base.md §3.
Anti-patterns to flag immediately
Anti-pattern
Severity
Reference
await openai.chat.completions.create(...) not wrapped in traceAICall()
