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skill execution layer for exellix ecosystem using @x12i/ai-gateway with FlexMD 2.0 support and Catalox as the catalog store","maintainers":[{"name":"exellix","email":"npm@exellix.com"}],"readme":"# @exellix/ai-skills\r\n\r\nFoundational skill execution layer for the exellix ecosystem: templates live in the **Catalox** native catalog **`ai-skills`**, execution uses **`@x12i/ai-gateway`**, and responses support **FlexMD 2.0** structured text.\r\n\r\n🚀 **Env-Ready**: Provider keys can be loaded from `.env` automatically. **`catalox` is required** at construction time (see Quick Start).\r\n\r\n> **Catalox 6:** Skill catalogs bootstrap via **Mongo** (`ensureCataloxFromEnv` / `createCataloxFromEnvAsync` from **`@exellix/catalox-ai-skills`**). Env: **`MONGO_URI`** or **`MONGODB_URI`**. See [**`documenations/catalox-6-rollout.md`**](../documenations/catalox-6-rollout.md).\r\n\r\n## Documentation\r\n\r\n| Topic | Doc |\r\n|--------|-----|\r\n| Flex-MD vs gateway parsing, integration tests | **[docs/FLEX_MD_AND_TESTING.md](docs/FLEX_MD_AND_TESTING.md)** |\r\n| Catalox integration & env notes | **[docs/CATALOX_PEER_GUIDE.md](docs/CATALOX_PEER_GUIDE.md)** · [Environment (Mongo & Catalox 6)](#environment-mongo--catalox-6) |\r\n| Activix integration best practices | **[.docs/activix-integration-best-practices-checklist.md](.docs/activix-integration-best-practices-checklist.md)** |\r\n| Per-package log level (`AI_SKILLS_LOGS_LEVEL`) | **[Logging](#logging-aiskills--x12ilogxer)** below · [Logxer integration checklist](docs/LOGXER_INTEGRATION_CHECKLIST.md) · [@x12i/logxer on npm](https://www.npmjs.com/package/@x12i/logxer) |\r\n| Gateway templates (v4), `invoke()` vs `invokeChat()`, `templateRendering` | **[docs/GATEWAY_TEMPLATE_PROTOCOL_V4.md](docs/GATEWAY_TEMPLATE_PROTOCOL_V4.md)** |\r\n| This package: `workingMemory`, `templateRenderOptions`, client `templateRendering` | **[docs/AI_SKILLS_GATEWAY_TEMPLATES.md](docs/AI_SKILLS_GATEWAY_TEMPLATES.md)** |\r\n| **Upgrading to `@x12i/ai-gateway` 10.x** (billing, required fields, removed behaviors) | **[docs/UPGRADING_AI_GATEWAY_10.md](docs/UPGRADING_AI_GATEWAY_10.md)** |\r\n| Invoke execution metadata (`provider`, `modelUsed`, `effectiveModelConfig`, …) | **[docs/AI_GATEWAY_INVOKE_EXECUTION_METADATA.md](docs/AI_GATEWAY_INVOKE_EXECUTION_METADATA.md)** |\r\n| Wire model on `runSkill()` (required; no alias / no gateway default) | **[docs/MODEL_INVOKE_CONTRACT.md](docs/MODEL_INVOKE_CONTRACT.md)** |\r\n| Host invoke contract (`reasoningEffort`, no caller `maxTokens`) | **[Host responsibilities](#host-responsibilities-orchestrator--main)** · [MODEL_INVOKE_CONTRACT.md](docs/MODEL_INVOKE_CONTRACT.md) |\r\n| OpenRouter vs direct routing (`PREFER_OPENROUTER`) — not implemented here | **[docs/PREFER_OPENROUTER_ROUTING.md](docs/PREFER_OPENROUTER_ROUTING.md)** |\r\n| Graph execution context (`graphId`, `nodeId`, identity mapping) | **[docs/GRAPH_EXECUTION_SUPPORT.md](docs/GRAPH_EXECUTION_SUPPORT.md)** |\r\n| Skills manager: preflight, FuncX, authoring (not in this package) | **[docs/SKILLS_MANAGER_CLIENT_GUIDE.md](docs/SKILLS_MANAGER_CLIENT_GUIDE.md)** |\r\n| External follow-ups (Activix persistence vs gateway envelope) | **[docs/AI_GATEWAY_FEATURE_REQUESTS.md](docs/AI_GATEWAY_FEATURE_REQUESTS.md)** |\r\n| Gateway server tools (OpenRouter pass-through) | **[docs/GATEWAY_SERVER_TOOLS.md](docs/GATEWAY_SERVER_TOOLS.md)** · [OPENROUTER_SERVER_TOOLS_WITH_AI_SKILLS.md](docs/OPENROUTER_SERVER_TOOLS_WITH_AI_SKILLS.md) · [upstream CR for gateway](docs/AI_GATEWAY_SERVER_TOOLS_CR.md) |\r\n\r\n## Features\r\n\r\n- **Unified Skill Runner**: Execute skills via metadata, not hardcoded handlers\r\n- **FlexMD 2.0 Output**: Always returns structured-text format with parsed payloads\r\n- **Catalox-native templates**: Instruction and prompt bodies are read from the Firestore native catalog **`ai-skills`** via **`@x12i/catalox`**; the gateway receives **inline template text** (not nx-content registry keys for skills)\r\n- **Catalox provisioning**: `npm run catalox:provision-ai-skills` upserts catalog metadata + Optimixer fields from **`AI_SKILLS_CATALOG_ITEMS`** (preserves template bodies already stored in Catalox)\r\n- **Presentation API**: Load pretty markdown for editors, save with lossless storage normalization (`getSkillTemplatesForPresentation`, `updateSkillTemplatesFromPresentation`, `getSkillTemplateInputs`); see [Skill templates (Catalox)](#skill-templates-catalox)\r\n- **Catalog audit fields (storage only)**: Catalox rows may store optional audit template text for editors; this package **only** executes **`runSkill`** (no built-in audit pass).\r\n- **Gateway integration**: `@x12i/ai-gateway` for LLM calls only; skill templates are **never** loaded from nx-content / GitHub — Catalox inline strings on **`invoke()`** only\r\n- **Activity tracking**: With `enableActivityTracking`, the gateway persists activities via **`@x12i/activix`**; this SDK enriches rows with **`cost` / `costStatus`** and contract-shaped **`outer.output.parsed`** when applicable (see [Cost and output contract](#cost-and-output-contract-run-analysis))\r\n- **Model Configuration**: Per-request model selection and sampling parameters (`temperature`, `topP`, `reasoningEffort`); completion token budget is Optimixer-owned (see [Host responsibilities](#host-responsibilities-orchestrator--main))\r\n- **Full template pipeline (default)**: Uses `gateway.invoke()` with inline bodies; the gateway message builder renders tokens (downstream stack; e.g. Rendrix). `runSkill` populates `workingMemory.input` and can pass `templateRenderOptions`, `templateTokens`, or client-level `templateRendering` (see **[docs/AI_SKILLS_GATEWAY_TEMPLATES.md](docs/AI_SKILLS_GATEWAY_TEMPLATES.md)**).\r\n- **Execution engine catalog**: Optional **`aiEngineId`** echoes the Catalox **`ai-engines`** row (**`ai-gateway`** only; provision with **`npm run catalox:provision-ai-engines`**) for discovery and **`identity`** — see [Execution engines (`aiEngineId`)](#2b-execution-engines-aiengineid).\r\n- **Execution-only scope**: Preflight / authoring / FuncX invoke review live in **skills manager** clients — see **[docs/SKILLS_MANAGER_CLIENT_GUIDE.md](docs/SKILLS_MANAGER_CLIENT_GUIDE.md)**.\r\n\r\n## Installation\r\n\r\n```bash\r\nnpm install @exellix/ai-skills\r\n```\r\n\r\nThis package depends on **`@x12i/ai-gateway`**, **`@x12i/catalox`**, **`@x12i/logxer`**, **`@x12i/env`**, **`@x12i/rendrix`**, and **`@exellix/catalox-ai-skills`** (Mongo Catalox bootstrap). **`@x12i/catalox` ≥ 6.0** expects **Node 20+**.\r\n\r\n**`@x12i/ai-gateway` ≥ 11.0.0** (openrouter-runtime **1.0.4**, `@x12i/ai-tools` **3.3.5**) (see `package.json`) with **`@x12i/activix` ^9.0** and **`@x12i/ai-profiles` ^3.4** is required: every invoke needs an explicit wire **`model`** and **`maxTokens`** (this package supplies **`maxTokens`** via **`@x12i/optimixer`** before `gateway.invoke()`). Invoke **`metadata`** includes routing fields (≥9.1.1), rejection **`metadata`** on failures (≥9.1.2), normalized billing (**`costUsd`**, **`cost`**, **`costStatus: priced | unpriced`**, optional **`costBreakdown`**) on success (≥9.3; ai-tools catalog pricing in gateway ≥10.1), and optional **OpenRouter server-tool metadata** (≥10.4.1). **This package does not use `@x12i/ai-tools` directly** — cost is resolved by the gateway. See **`docs/UPGRADING_AI_GATEWAY_10.md`** and **`docs/AI_GATEWAY_INVOKE_EXECUTION_METADATA.md`**. Every **`runSkill`** call must include **`agentId`**, **`jobTypeId`**, and **`taskTypeId`** (Activix linkage — no package defaults).\r\n\r\n**CI:** `npm run test:ci` runs **`npm run build`** and **`npm run test:unit`** (no live Firestore or real LLM). Full **`npm test`** still runs **`test:integration`**, which may append live catalog tests when env enables them.\r\n\r\nCustom admin flows that call Catalox with **`createCatalog`**, **`bindCatalogToApp`**, or similar before an app binding exists should set **`superAdmin: true`** on **`CataloxContext`** (see **`defaultAiSkillsCataloxContext`** overrides and **[docs/CATALOX_PEER_GUIDE.md](docs/CATALOX_PEER_GUIDE.md)**).\r\n\r\n**Publishing:** the npm tarball is limited to **`dist/`**, **`README.md`**, and **`erc-manifest.json`** via the **`files`** field in **`package.json`** (avoids shipping tests, logs, or local scratch files).\r\n\r\n## Environment variables (Mongo & Catalox 6)\r\n\r\n**`createCataloxFromEnvAsync()`** / **`ensureCataloxFromEnv()`** from **`@exellix/catalox-ai-skills`** delegate to **`@x12i/catalox/mongo`**.\r\n\r\n| Variable | Notes |\r\n|----------|--------|\r\n| **`MONGO_URI`** or **`MONGODB_URI`** | **Required** — Mongo connection string for Catalox catalogs |\r\n| **`MONGO_DB_NAME`** / **`MONGODB_DB`** | Optional database name (default `catalox`) |\r\n\r\nProvider keys for the default gateway (**`OPENAI_API_KEY`**, **`GROK_API_KEY`**, **`OPEN_ROUTER_KEY`** / **`OPENROUTER_API_KEY`**) are unchanged — see **Troubleshooting** below.\r\n\r\n## Quick Start\r\n\r\n### 0) Prerequisites\r\n\r\n1. **Node.js 20+** — matches **`@x12i/catalox`** engine requirements.\r\n2. **Mongo Catalox** — set **`MONGO_URI`** or **`MONGODB_URI`** (see [above](#environment-mongo--catalox-6)). Use **`await createCataloxFromEnvAsync()`** or pass your own **`Catalox`** from **`createCataloxFromMongoEnv`**.\r\n3. **Provision the catalog** (creates/updates Catalox app bindings, descriptors, and skill rows):\r\n\r\n```bash\r\nnpm run catalox:provision-ai-skills\r\nnpm run catalox:provision-ai-engines\r\nnpm run catalox:verify\r\n```\r\n\r\n4. At least one **LLM provider** key (`OPENAI_API_KEY`, `GROK_API_KEY`, or `OPEN_ROUTER_KEY` / `OPENROUTER_API_KEY`) for **`@x12i/ai-gateway`**.\r\n\r\n### 1) Create the client\r\n\r\n```typescript\r\nimport { ExellixSkillsClient, RunSkillRequest, RunSkillResponse, createCataloxFromEnv } from \"@exellix/ai-skills\";\r\n\r\nconst catalox = createCataloxFromEnv();\r\n\r\nconst skills = new ExellixSkillsClient({\r\n  catalox,\r\n  enableActivityTracking: true,\r\n});\r\n```\r\n\r\nYou can also construct **`Catalox`** yourself (`createCatalox` from **`@x12i/catalox`** or **`@x12i/catalox/embedder`** with your **`firestore`** / **`firebaseApp`**) and pass **`options.catalox`**. An external **`gateway`** is optional; **`catalox` is always required**.\r\n\r\n### 2) Run a skill\r\n\r\n```typescript\r\n// Using the RunSkillRequest interface\r\nconst runSkillRequest: RunSkillRequest = {\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Question: What is the best way to migrate from X to Y?\",\r\n  variables: { orgName: \"ACME\", audience: \"security team\" },\r\n  jobId: \"job-123\",\r\n  taskId: \"task-456\",\r\n  agentId: \"agent-abc\",\r\n  jobTypeId: \"your-job-type-id\",\r\n  taskTypeId: \"your-task-type-id\",\r\n};\r\n\r\nconst res: RunSkillResponse = await skills.runSkill(runSkillRequest);\r\n\r\n// Access FlexMD payloads\r\nconsole.log(res.flexMd.payloads.shortAnswer);\r\nconsole.log(res.flexMd.payloads.fullAnswer);\r\n```\r\n\r\n### 2b) Execution engines (`aiEngineId`)\r\n\r\n**`runSkill`** always invokes **`@x12i/ai-gateway`** with inline Catalox template text; **Rendrix runs inside the gateway**. Optional **`aiEngineId`** must be omitted, blank, or **`ai-gateway`** — it is merged into **`gateway.invoke`** **`identity`** and matches the single row in the Catalox **`ai-engines`** catalog (provisioned for listing / UIs). Unknown values throw at resolve time.\r\n\r\n### 2a) Run a skill with model configuration\r\n\r\n```typescript\r\n// Override model and generation parameters per-request\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Question: What is the best way to migrate from X to Y?\",\r\n  variables: { orgName: \"ACME\" },\r\n  jobId: \"job-123\",\r\n  taskId: \"task-456\",\r\n  agentId: \"agent-abc\",\r\n  jobTypeId: \"your-job-type-id\",\r\n  taskTypeId: \"your-task-type-id\",\r\n  modelConfig: {\r\n    model: \"gpt-4-turbo\",\r\n    temperature: 0.7,\r\n    topP: 0.9,\r\n    reasoningEffort: \"medium\",\r\n  }\r\n});\r\n```\r\n\r\n## Host responsibilities (orchestrator ↔ MAIN)\r\n\r\nApplies to **`@exellix/ai-tasks` 8.4+** and other graph orchestrators calling **`runSkill()`** for the MAIN skill phase.\r\n\r\n| Send | Do not send |\r\n|------|-------------|\r\n| Wire **`model`** / **`modelId`** (required) | **`maxTokens`**, **`max_completion_tokens`**, **`max_output_tokens`** on `modelConfig` |\r\n| Optional **`temperature`**, **`topP`**, **`reasoningEffort`**, **`frequencyPenalty`**, **`presencePenalty`**, **`retry`**, **`timeoutMs`** | Profile / plan model names — resolve in `@exellix/ai-tasks` only |\r\n| Optional **`serverTools`**, **`openrouter.apiMode`** (top-level or on `modelConfig`) | Raw OpenRouter tool payloads, provider secrets |\r\n| Optional **`provider`** | Expectation that ai-skills picks a default model |\r\n\r\n**Token budget:** owned by **`@x12i/optimixer`** from Catalox catalog + prompts. After invoke, read **`metadata.usage.maxTokensRequested`** (and trace **`usage.maxTokensRequested`** when trace mode is on).\r\n\r\n**`reasoningEffort`:** optional on **`modelConfig`**. Request value overrides Catalox **`optimixerReasoningEffort`** for that skill key; omit to keep catalog-only behavior. Resolution order: request → catalog → `\"not-applicable\"`.\r\n\r\nFull rules: **[docs/MODEL_INVOKE_CONTRACT.md](docs/MODEL_INVOKE_CONTRACT.md)** · observability follow-ups: **[archived ai-skills-llm-observability.md](../archived/ai-skills/docs/legacy/ai-skills-llm-observability.md)**.\r\n\r\n### 2b) Optional: parser / template overrides\r\n\r\nPer skill run you can pass **`templateRenderOptions`** (merged on gateway defaults) and **`templateTokens`** (highest-priority overlay). On the client, **`templateRendering`** sets defaults for every invoke. Details and examples: **[docs/AI_SKILLS_GATEWAY_TEMPLATES.md](docs/AI_SKILLS_GATEWAY_TEMPLATES.md)**.\r\n\r\n### 2c) Optional: identity propagation\r\n\r\n**`identity` is per-request runtime context** from your application (trace ids, tenant, graph/node ids, etc.). It is **not** the SDK client’s constructor options, **not** env/config for this package, and **not** “package identity” from `package.json`—unless you explicitly copy such values into this object when you build the request.\r\n\r\n**`identity` vs `runContext`:** The gateway **`invoke()`** API uses the property name **`identity`** (see `@x12i/ai-gateway`). Activix stores that same envelope on activity documents as BSON **`runContext`**. This SDK passes **`identity`** through to the gateway; you do **not** set `runContext` on `runSkill` inputs here. Responses expose the envelope as **`identity`** when the gateway returns it (and resolution may also consider metadata shapes that use `runContext`).\r\n\r\nYou can pass an **`identity`** object to `runSkill()` to propagate caller identity context downstream.\r\n\r\n- If you provide `identity`, the client forwards it **as-is** to the gateway.\r\n- The client will add **`identity.skillId`** from the executing request **only if** it is missing.\r\n- The `RunSkillResponse` includes `identity` **only if downstream returns one**. If downstream does not provide `identity`, it will be omitted (no fallback).\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Question: ...\",\r\n  jobId: \"job-123\",\r\n  taskId: \"task-456\",\r\n  agentId: \"agent-abc\",\r\n  jobTypeId: \"your-job-type-id\",\r\n  taskTypeId: \"your-task-type-id\",\r\n  skillId: \"node-q0\",\r\n  identity: {\r\n    traceId: \"trace-123\",\r\n    userId: \"user-456\"\r\n    // skillId is optional here; if omitted, it will be set from `skillId` above\r\n  }\r\n});\r\n\r\n// Only present when downstream returned it:\r\nconsole.log(res.identity);\r\n\r\n// Token usage + cost (from gateway metadata); always on metadata.usage for every run.\r\n// costUsd when priced; costStatus when usage exists but no price (\"unpriced\") or async pricing (\"deferred\").\r\nconsole.log(res.metadata.usage, res.metadata.costUsd, res.metadata.costStatus, res.metadata.modelUsed);\r\n\r\n// When `identity` is returned, the same snapshot is nested under `identity.aiSkillsLlm` for Activix-style\r\n// chaining—forward the whole `identity` to the next `runSkill` so pipelines retain billing context.\r\nconsole.log(res.identity?.aiSkillsLlm);\r\n```\r\n\r\n### 3) Model wire id, Optimixer budget, and trace visibility\r\n\r\n**Wire model (mandatory):** Every `runSkill()` must include **`modelConfig.model`** or **`modelConfig.modelId`** as the **concrete provider wire id** resolved upstream (`@exellix/ai-tasks` / `resolveInvocationPlan`). Missing model is **rejected** before invoke; non-wire profile input is handled by **`@x12i/ai-gateway`** / **`@x12i/ai-tools`**, not duplicated here. **No gateway default model** in this package. See **[docs/MODEL_INVOKE_CONTRACT.md](docs/MODEL_INVOKE_CONTRACT.md)**.\r\n\r\n**Completion token budget (Optimixer-owned):** Caller `maxTokens` / `max_completion_tokens` / `max_output_tokens` are **stripped** (anomaly log). Budget is predicted by **`@x12i/optimixer`** from Catalox catalog fields and resolved **`reasoningEffort`**, then merged into `modelConfig` before `gateway.invoke()`.\r\n\r\n**Max-token retry (MAIN hop):** When the provider returns a limit **`finishReason`** (e.g. `length`) and Optimixer **`completeAiMaxTokensPrediction`** returns **`retryPrediction`**, this package **re-invokes the gateway once** with Optimixer’s bumped **`recommendedMaxTokens`** (caller ceilings still apply via existing merge). Up to **one** retry; observability includes **`metadata.optimixerRetryCount`** and log code **`OPTIMIXER_MAX_TOKENS_RETRY`**. No local token multipliers or re-predict bypass.\r\n\r\n**Other `modelConfig` fields** (`temperature`, `topP`, `reasoningEffort`, `provider`, `serverTools`, `openrouter`, …) are forwarded when set.\r\n\r\n### 3b) Gateway server tools (OpenRouter pass-through)\r\n\r\n`@exellix/ai-skills` forwards OpenRouter **server-tool intent** to **`@x12i/ai-gateway` (≥ 10.4.4)**. This package does **not** execute OpenRouter tools locally and does **not** depend on `@x12i/openrouter-runtime`.\r\n\r\n- Set **`serverTools`** on the request, on **`modelConfig`**, in Catalox **`data.runtime.serverTools`**, or via client **`defaultServerTools`** (merge order: client → catalog → request → modelConfig).\r\n- Tool usage, citations, generated images, and patch proposals appear on **`RunSkillResponse.metadata`** (not in FlexMD unless the skill template asks for them).\r\n- No tools are enabled by default; existing skills behave unchanged unless you opt in.\r\n\r\nSee **[docs/GATEWAY_SERVER_TOOLS.md](docs/GATEWAY_SERVER_TOOLS.md)** · **[docs/OPENROUTER_SERVER_TOOLS_WITH_AI_SKILLS.md](docs/OPENROUTER_SERVER_TOOLS_WITH_AI_SKILLS.md)**.\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"What changed in the vendor docs?\",\r\n  jobId: \"job-1\",\r\n  taskId: \"task-1\",\r\n  agentId: \"agent-1\",\r\n  jobTypeId: \"research\",\r\n  taskTypeId: \"answer\",\r\n  modelConfig: {\r\n    provider: \"openrouter\",\r\n    model: \"openai/gpt-5.2\",\r\n    serverTools: {\r\n      webSearch: { mode: \"required\", engine: \"auto\", maxResults: 5 },\r\n      webFetch: { mode: \"allowed\", maxContentTokens: 50000 },\r\n    },\r\n  },\r\n});\r\n\r\nconsole.log(res.flexMd.payloads);\r\nconsole.log(res.metadata.citations);\r\nconsole.log(res.metadata.serverTools);\r\n```\r\n\r\n| What you need | Where it appears |\r\n|---------------|------------------|\r\n| What was **sent toward invoke** (merged `modelConfig`, `timeoutMs`) | Trace-only: **`debugTrace.invokeRequest`** when `executionMode: \"trace\"` or `diagnostics.includeDebugTrace` |\r\n| Effective completion cap | **`metadata.usage.maxTokensRequested`** · trace **`debugTrace.usage.maxTokensRequested`** |\r\n| Effective **`reasoningEffort`** | Trace **`debugTrace.invokeRequest.modelConfig.reasoningEffort`** (request override or catalog default) |\r\n| What **ran** (model id, token counts) | **`debugTrace.modelUsed`**, **`debugTrace.usage`**, routing ids |\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Question: ...\",\r\n  jobId: \"job-1\",\r\n  taskId: \"task-1\",\r\n  agentId: \"agent-abc\",\r\n  jobTypeId: \"your-job-type-id\",\r\n  taskTypeId: \"your-task-type-id\",\r\n  modelConfig: {\r\n    model: \"gpt-4-turbo\",\r\n    temperature: 0.5,\r\n    reasoningEffort: \"high\",\r\n  },\r\n  executionMode: \"trace\",\r\n});\r\n\r\nconsole.log(res.debugTrace?.invokeRequest?.modelConfig?.reasoningEffort);\r\nconsole.log(res.metadata.usage?.maxTokensRequested);\r\nconsole.log(res.debugTrace?.usage.maxTokensRequested);\r\n```\r\n\r\nOn gateway failures with trace mode enabled, **`SkillExecutionTraceError`** carries **`diagnostics.trace`** with the same shapes where available; metadata attached on the thrown error is merged when present (see **`metadataFromInvokeError`** export).\r\n\r\n### Cost and output contract (Run Analysis)\r\n\r\nStudio / graph Run Analysis expects activities and responses to explain billing and structured output. This package normalizes both on every successful **`runSkill`** and best-effort patches the persisted Activix row when **`metadata.activityId`** is present.\r\n\r\n#### Cost reporting (`metadata.costUsd` / `metadata.costStatus`)\r\n\r\nGateway **`invoke()`** metadata (≥ **9.3**, ai-tools catalog in gateway ≥ **10.1**) uses **`costUsd`** / **`cost`** when priced and **`costStatus: \"priced\" | \"unpriced\"`**. Optional **`costBreakdown`** (prompt/completion USD) is forwarded when the gateway catalog pricing includes it. **This SDK never calculates cost locally** — it normalizes gateway metadata only. Priced runs expose **`costUsd` only** (gateway **`\"priced\"`** status is not echoed). When usage exists but no USD total is known, **`costStatus: \"unpriced\"`** is set (gateway explicit flag or SDK fallback).\r\n\r\n| Gateway / router situation | Gateway `metadata` | On `RunSkillResponse.metadata` | Activix record (gateway `logSuccess` + SDK patch) |\r\n|----------------------------|-------------------|-------------------------------|------------------------------------------------------|\r\n| Priced (router or ai-tools catalog) | `costUsd`, `cost`, `costStatus: \"priced\"`, optional `costBreakdown` | `costUsd`, optional `costBreakdown` (no `costStatus`) | Root `cost` / `costUsd` / `costStatus: \"priced\"`, **`outer.cost.usd`**; prefer these over `outer.metadata` for billing |\r\n| Usage, no price | `costStatus: \"unpriced\"` | `costStatus: \"unpriced\"` | Root `costStatus: \"unpriced\"` + token usage on root `metadata` / `response.metadata` |\r\n| Legacy async pricing | `costStatus: \"deferred\"` (older gateways) | `costStatus: \"deferred\"` | same |\r\n\r\nGateway **`logSuccess`** writes billing on the activity row during **`invoke()`** when **`enableActivityTracking`** is on (default). **`patchActivixActivityAfterSkill`** then merges the same billing slice plus **`outputContract`** **`parsed`** fields so Run Analysis and Activix queries stay aligned with **`RunSkillResponse.metadata`**.\r\n\r\n`metadata.usage` is always populated (zeros when the gateway omits token fields). The same billing slice is mirrored on **`identity.aiSkillsLlm`** when the gateway returns **`identity`**, and on **`debugTrace`** in trace mode.\r\n\r\n#### Output contract (`outputContract`)\r\n\r\n**`outputContract` is per invoke**, not a global list for all skills. Pass the field names your graph node or skill expects on **`parsed`** / **`outer.output.parsed`** — typically from graph **`inputs.outputContract`** (via **`@exellix/ai-tasks`** / graph-engine).\r\n\r\nSupported shapes: `string[]`, `{ fields: string[] }`, `{ keys: string[] }`, or `{ required: string[] }`.\r\n\r\nAfter FlexMD / gateway parsing (and local **`parseFlexMd`** fallback), the SDK fills **missing** contract keys from:\r\n\r\n1. **`flexMd.payloads`**\r\n2. Markdown section headings (`### Short Answer` → `shortAnswer`)\r\n\r\nExamples (illustrative — use the contract for **that** skill/node):\r\n\r\n```typescript\r\n// professional-answer–style skill\r\nawait skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  outputContract: [\"shortAnswer\", \"fullAnswer\", \"assumptions\", \"unknowns\", \"evidence\"],\r\n  // ...jobId, taskId, agentId, jobTypeId, taskTypeId, input, ...\r\n});\r\n\r\n// professional-decision–style skill\r\nawait skills.runSkill({\r\n  skillKey: \"skills/professional-decision\",\r\n  outputContract: [\"decision\", \"score\", \"rationale\", \"risks\"],\r\n  // ...\r\n});\r\n```\r\n\r\nThis package does **not** auto-map **`skillKey`** to a catalog contract; upstream must pass **`outputContract`** when structured fields are required. Catalog payload names are documented in **[docs/metadata.md](docs/metadata.md)** (`requiredPayloads` per skill).\r\n\r\n**Exports** (for orchestrators and tests): `normalizeSkillBillingFromGatewayMetadata`, `enrichParsedForOutputContract`, `patchActivixActivityAfterSkill`, types **`SkillCostStatus`**, **`OutputContract`**.\r\n\r\n## Direct functions (re-consider)\r\n\r\nTyped wrappers on **`ExellixSkillsClient`** that call revision skills with nested **`templateTokens`**. Shared runtime: **`ReConsiderationRuntime`** (`jobId`, `taskId`, `agentId`, `jobTypeId`, `taskTypeId`, `modelConfig`). Object fields (`context`, `providedContext`, etc.) are stringified via **`stringifyForPrompt`** so they never become `[object Object]`. Callers own merge/apply of returned fragments.\r\n\r\n| Method | Skill | Result |\r\n|--------|-------|--------|\r\n| **`fillReConsiderationGap`** | `re-consider.fill-gap` | `{ gapOutput }` |\r\n| **`reviewReConsiderationResponse`** | `re-consider.review` | `{ reviewStatus, reviewOutput }` |\r\n| **`rewriteReConsiderationScope`** | `re-consider.rewrite-scope` | `{ correctedScope }` |\r\n| **`rewriteReConsiderationFull`** | `re-consider.rewrite-full` | `{ correctedAnswer }` |\r\n| **`calibrateReConsiderationDecision`** | `re-consider.calibrate-decision` | `{ calibratedOutput }` |\r\n\r\n```typescript\r\nconst result = await skills.fillReConsiderationGap(\r\n  {\r\n    input: \"What are the risks?\",\r\n    originalInstructions: \"Answer with evidence.\",\r\n    originalResponse: priorAnswer,\r\n    gapExplained: \"Missing evidence bullets for Section B.\",\r\n    fragmentName: \"Section B\",\r\n    context: { assetId: \"db-1\" },\r\n  },\r\n  {\r\n    jobId: \"job-1\",\r\n    taskId: \"task-1\",\r\n    agentId: \"agent-1\",\r\n    jobTypeId: \"job-type\",\r\n    taskTypeId: \"task-type\",\r\n    modelConfig: { model: \"gpt-4o-mini\" },\r\n  }\r\n);\r\n// result.gapOutput — insert or replace; do not expect a merged full response\r\n```\r\n\r\nToken roots (examples): `taskVariables.gap.*` / `jobMemory.gap.*`, `taskVariables.review.*` / `jobMemory.review.*`, `taskVariables.scope.*` / `jobMemory.scope.*`, `taskVariables.full.*` / `jobMemory.full.*`, `taskVariables.calibration.*` / `jobMemory.calibration.*`, plus `jobMemory.providedContext` and `{{input}}`. Chain prior outputs via optional `gapResults` / `reviewResults` / `rewriteResults` / `calibrationResults`.\r\n\r\nAlso exported as standalone functions (and request builders) from **`@exellix/ai-skills`**.\r\n\r\n## Built-in skills (catalog)\r\n\r\nAuthoritative rows are defined in code as **`AI_SKILLS_CATALOG_ITEMS`** and written to Catalox by **`npm run catalox:provision-ai-skills`**. Typical runnable keys:\r\n\r\n- **`skills/professional-answer`** — structured professional answer (FlexMD payloads)\r\n- **`skills/re-consider.fill-gap`** — fill a known gap (`usageHints.skillMode: \"revision\"`)\r\n- **`skills/re-consider.review`** — review response → `reviewStatus` + `reviewOutput`\r\n- **`skills/re-consider.rewrite-scope`** — rewrite one selected scope → `correctedScope`\r\n- **`skills/re-consider.rewrite-full`** — full rewrite → `correctedAnswer`\r\n- **`skills/re-consider.calibrate-decision`** — calibrate decision/priority/ranking → `calibratedOutput`\r\n- Additional catalog rows / forks: use **`listCatalogSkills()`**, **`listPublishedSkills()`**, or Catalox **`listCatalogItems`** for the live list.\r\n\r\n`runSkill` takes **`skillKey`** like `skills/professional-answer`. Bodies are **not** resolved as nx-content registry keys for execution; they are **loaded from Catalox** and sent to the gateway as **inline template strings**.\r\n\r\n## Skill templates (Catalox)\r\n\r\n### Provisioning (metadata + Optimixer → Catalox)\r\n\r\n`npm run catalox:provision-ai-skills` upserts rows from **`AI_SKILLS_CATALOG_ITEMS`** (title, description, Optimixer fields, `status`). It **preserves** existing **`instructionsText` / `promptText`** already in Catalox — template bodies are authored in Catalox only (via **`updateSkillTemplatesFromPresentation`**, **`upsertSkillCatalogItem`**, or your admin tooling).\r\n\r\n```bash\r\nnpm run catalox:provision-ai-skills\r\nnpm run catalox:verify\r\n```\r\n\r\nRunnable skills need both bodies in Catalox and **`status`** `draft` or `published` (computed from body completeness + **`catalogReleaseStatus`** on provision).\r\n\r\n### Catalog manager API (skills studio / admin)\r\n\r\nFor apps that manage the catalog without running LLMs, use **`createAiSkillsCatalogApi({ catalox })`** — full CRUD + fork. See **[docs/AI_SKILLS_CATALOG_MANAGER.md](./docs/AI_SKILLS_CATALOG_MANAGER.md)**.\r\n\r\n| Function | Purpose |\r\n|----------|---------|\r\n| **`listSkills` / `listSubSkills`** | List base and sub-skill rows |\r\n| **`getSkill` / `getSkillContent`** | Read metadata and template markdown |\r\n| **`createSkill` / `updateSkill`** | Create and patch rows |\r\n| **`forkSkill`** | Sub-skill fork with optional overrides |\r\n| **`deleteSkill`** | Hard-delete sub-skills; soft-delete base skills |\r\n| **`publishSkill` / `unpublishSkill`** | Release status shortcuts |\r\n\r\nLower-level **`ExellixSkillsClient`** methods below remain available when you already use the execution client.\r\n\r\n### Presentation layer (read / edit)\r\n\r\nLow-level helpers live under **`@exellix/ai-skills`** exports from **`./catalox`** (e.g. **`normalizeForStorage`**, **`toPresentationMarkdown`**, **`extractTemplateTokensFromTexts`**).\r\n\r\nOn **`ExellixSkillsClient`**:\r\n\r\n| Method | Purpose |\r\n|--------|---------|\r\n| **`getSkillTemplatesForPresentation(skillKey, { includeAudit? })`** | Catalox read → markdown formatted for editors |\r\n| **`updateSkillTemplatesFromPresentation(skillKey, patch, options?)`** | Editor markdown → lossless storage normalization → Catalox upsert (requires **write** Catalox context) |\r\n| **`getSkillTemplateInputs(skillKey)`** | Union of **`{{token}}`** placeholders from raw stored bodies; treats **`input`** as the primary payload token |\r\n| **`resolveRawTemplate(skillKey, section, { role, strategyKey? })`** | Raw Catalox bodies for PRE synthesis / orchestration / audit roles (used by `@exellix/ai-tasks`) |\r\n| **`resolveSynthesisContextStrategyKey(skillKey)`** | Default Catalox **`synthesisContext`** template key from **`supportedCapabilities.synthesisContext`** (graphenix 2.1.0 — plan wire must not carry `unitParams.synthesisInputStrategy`) |\r\n| **`listCatalogSkills(options?)`** | Lists every catalog row (all **`status`** values); forwards Catalox query options (e.g. **`limit`**) |\r\n| **`upsertSkillCatalogItem(input, options?)`** | Create/update full row (metadata + optional markdown); optional **`ifNotExists`** for strict create |\r\n| **`softDeleteSkillCatalogItem(skillKey)`** | Clears bodies and audit fields and sets **`planned`** (no hard delete) |\r\n\r\n**Write access:** updates use **`batchUpsertNativeCatalogItems`**; use a Catalox context with permission to write the catalog (e.g. provision-style god mode or an app binding with write). This package does not create credentials.\r\n\r\n### Skill key usage\r\n\r\n```typescript\r\nawait skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Your question here...\",\r\n});\r\n```\r\n\r\nCatalox item ids are the short key (e.g. `professional-answer`); the client accepts either `skills/professional-answer` or the short id where helpers normalize.\r\n\r\n### Troubleshooting\r\n\r\n##### ❌ `options.catalox is required`\r\n\r\nPass **`catalox`** into **`new ExellixSkillsClient({ catalox, ... })`**. Use **`createCataloxFromEnv()`** from this package, **`createCataloxFromEnv()`** from **`@x12i/catalox/firebase`**, or **`createCatalox({ firestore, firebaseApp })`** from **`@x12i/catalox`** / **`@x12i/catalox/embedder`**.\r\n\r\n##### ❌ Skill is `planned` or “incomplete templates”\r\n\r\nRun **`npm run catalox:provision-ai-skills`** (or write bodies via **`updateSkillTemplatesFromPresentation`**) so **`instructionsText`** and **`promptText`** are non-empty and **`status`** is **`draft`** or **`published`**.\r\n\r\n##### ❌ Provider / env validation\r\n\r\nAt least one of **`OPENAI_API_KEY`**, **`GROK_API_KEY`**, **`OPEN_ROUTER_KEY`**, **`OPENROUTER_API_KEY`** is required when the client builds the default gateway. Firebase / Catalox variables are summarized [above](#environment-firebase--catalox-v4).\r\n\r\n### Logging (`AiSkills` / `@x12i/logxer`)\r\n\r\nThis package uses **[`@x12i/logxer`](https://www.npmjs.com/package/@x12i/logxer)** with three stable identifiers:\r\n\r\n| Field | Value | Purpose |\r\n|-------|--------|---------|\r\n| **`packageName`** | `AiSkills` | `package` column in logs, shadow, Mongo |\r\n| **`envPrefix`** | `AI_SKILLS` | Env vars and stack keys |\r\n| **`debugNamespace`** | `ai-skills` | `DEBUG=` filter slug |\r\n\r\nExported constants: `AI_SKILLS_LOGXER_PACKAGE`, `AI_SKILLS_LOGXER_ENV_PREFIX`, `AI_SKILLS_LOGXER_DEBUG_NAMESPACE`, `AI_SKILLS_LOGXER_PACKAGE_CONFIG`, `createAiSkillsLogger` (alias `createAiSkillsLogxer`).\r\n\r\n| Item | Detail |\r\n|------|--------|\r\n| **Canonical env var** | **`AI_SKILLS_LOGS_LEVEL`** |\r\n| **Fallback** | **`AI_SKILLS_LOG_LEVEL`** is used only if **`AI_SKILLS_LOGS_LEVEL`** is unset |\r\n| **Default** (both unset) | **`warn`** — not silent; you will see warnings and errors |\r\n| **Silence this package** | Set **`AI_SKILLS_LOGS_LEVEL=off`** (or `none` / `silent`) |\r\n| **More detail** | `info`, `debug`, or `verbose` (case-insensitive; see **@x12i/logxer** docs) |\r\n| **Full payloads** | Set **`AI_SKILLS_VERBOSE=true`** *and* **`AI_SKILLS_LOGS_LEVEL=verbose`** — attaches sanitized gateway request/response on `runSkill()` |\r\n| **Bulk stack config** | **`LOGXER_PACKAGE_LEVELS=AI_SKILLS:debug,...`** (host calls `applyPackageLogLevelsFromEnv()` at startup) |\r\n\r\nCross-cutting sinks (console, file, JSON, unified app config) are configured by the host app, not per this prefix — see the **@x12i/logxer** README.\r\n\r\nConsole output includes **`app:`** (host app from cwd `package.json`) and **`package:`** (`AiSkills` for lines from this library). Optional **`packageName`** on the client sets **`RuntimeIdentity.service`** only — it does not change the Logxer `package` field.\r\n\r\n**Per-path tiers:**\r\n\r\n| Path | `info` | `debug` | `verbose` + `AI_SKILLS_VERBOSE` |\r\n|------|--------|---------|-----------------------------------|\r\n| **`runSkill()`** | One completion summary (duration, provider, model, tokens) | Entry, Catalox fetch, invoke routing, FlexMD steps | Sanitized gateway request/response |\r\n| **`runSkill()`** | One completion summary per invoke | Gateway invoke, Optimixer merge | Response metadata / trace when enabled |\r\n| **Catalog / registry** | `discoverStructure`, `diagnose`, `runDiagnostics` completion only | List/probe/connection tests, client init/shutdown | N/A |\r\n\r\nFailures on invoke and config validation emit at **`error`** / **`warnCode`** with correlation ids.\r\n\r\n**`.env`:** with default **`autoLoadDotenv`**, the client loads **`.env`** from the current working directory using **`loadDotenv`** from **`@x12i/env`**.\r\n\r\n```bash\r\n# Threshold for this package\r\nexport AI_SKILLS_LOGS_LEVEL=info\r\n\r\n# Optional: full payloads (still requires _LOGS_LEVEL=verbose to print)\r\nexport AI_SKILLS_VERBOSE=true\r\n\r\n# Bulk stack config (loaded at startup)\r\nexport LOGXER_PACKAGE_LEVELS=AI_SKILLS:debug,AI_GATEWAY:warn\r\n```\r\n\r\nProgrammatic stack (logxer ≥ 4.5):\r\n\r\n```typescript\r\nimport { type StackLoggingOptions } from \"@x12i/logxer\";\r\nimport { ExellixSkillsClient, AI_SKILLS_LOGXER_ENV_PREFIX, createCataloxFromEnv } from \"@exellix/ai-skills\";\r\n\r\nconst logging: StackLoggingOptions = {\r\n  packageLevels: { [AI_SKILLS_LOGXER_ENV_PREFIX]: \"debug\" },\r\n};\r\n\r\nconst skills = new ExellixSkillsClient({\r\n  catalox: createCataloxFromEnv(),\r\n  logging,\r\n  verbose: true,\r\n});\r\n```\r\n\r\nFilter Mongo / shadow logs with `getJobLogs({ package: \"AiSkills\", ... })`.\r\n\r\n## Testing (integration + templates)\r\n\r\nThis repo is **ESM** (`\"type\": \"module\"`). Use the npm scripts below (they run via **`tsx`** for `.ts` entrypoints).\r\n\r\n**Default test gate (`npm test`):** **`npm run build`** then **`npm run test:integration`** (may append live catalog tests when env enables them). **`npm run test:ci`** runs **`npm run build`** and **`npm run test:unit`** (mocked gateway / no Firestore / no LLM).\r\n\r\n```bash\r\nnpm test                       # build + integration harness (see run.ts)\r\nnpm run test:ci                # build + deterministic unit slice (CI-safe)\r\nnpm run test:unit              # same unit slice as test:ci (without build)\r\nnpm run test:integration       # full integration harness (run.ts)\r\nnpm run catalox:provision-ai-skills   # upsert catalog metadata + Optimixer fields (before live tests)\r\nnpm run catalox:provision-ai-engines  # upsert in-repo engine rows → native `ai-engines`\r\nnpm run catalox:verify                # validate live Catalox rows for runtime requirements\r\nnpm run live:professional-answer      # smoke: professional-answer via Catalox + default gateway LLM\r\nnpm run live:re-consider              # smoke: all five re-consider direct functions via Catalox + LLM\r\nnpm run test:real              # extra real LLM scenarios; needs .env + valid Firebase credentials\r\n```\r\n\r\n#### Live tests (opt-in, `test/run.ts`)\r\n\r\nSome cases use **real Catalox** (Mongo-backed catalog) and the same integration slice can invoke **real LLM** calls when provider keys are set. They are **off by default** so CI does not require a provisioned project.\r\n\r\nSet **`AI_SKILLS_LIVE_TESTS=1`** (or `true` / `yes` / `on`) before **`npm run test:integration`** to append the live cases registered in **`test/run.ts`**:\r\n\r\n- **Catalox `ai-skills` list** — read-only catalog list sanity check.\r\n- **Catalox `ai-engines` list** — verifies **`npm run catalox:provision-ai-engines`** wrote the **`ai-gateway`** row (read-only).\r\n- **Re-consider direct functions** — real LLM calls for `fillReConsiderationGap`, `reviewReConsiderationResponse`, `rewriteReConsiderationScope`, `rewriteReConsiderationFull`, `calibrateReConsiderationDecision` (provision + sync templates first).\r\n\r\n**Unit mocks for re-consider** live only under **`test/`** (CI wiring via fake gateway). Production code and **`npm run live:re-consider`** use real Catalox + real LLM only — no mock fallbacks.\r\n\r\n**Why not in `@x12i/catalox`:** only **this package’s** test harness reads **`AI_SKILLS_LIVE_TESTS`**; Catalox does not gate downstream CI.\r\n\r\nOptionally run **`npm run test:real`** before a major release (broader LLM + client scenarios; needs Mongo + provider keys in **`.env`**).\r\n\r\n**Flex-MD log noise (`extractJsonFromFlexMd` / `require(...) is not a function`):** that path lives in **`@x12i/ai-gateway`**, not in this package’s `parseFlexMd`. See **[docs/FLEX_MD_AND_TESTING.md](docs/FLEX_MD_AND_TESTING.md)** for the split of responsibilities and acceptance notes.\r\n\r\n## Model Configuration\r\n\r\nYou can control which model is used and how it generates responses on a per-request basis using the `modelConfig` field.\r\n\r\n### Model Configuration Options\r\n\r\n```typescript\r\ninterface ModelConfig {\r\n  /** Model identifier (e.g., \"gpt-4-turbo\", \"claude-3-opus\", \"gpt-3.5-turbo\") */\r\n  model?: string;\r\n  \r\n  /** Model ID (alternative to model name, for provider-specific model IDs) */\r\n  modelId?: string;\r\n  \r\n  /** Provider name (e.g., \"openai\", \"anthropic\") */\r\n  provider?: string;\r\n  \r\n  /** Temperature for generation (0.0 to 2.0) - controls randomness */\r\n  temperature?: number;\r\n  \r\n  /** @deprecated Caller maxTokens is stripped — Optimixer sets completion budget from Catalox catalog. */\r\n  maxTokens?: never;\r\n  \r\n  /** Top-p (nucleus) sampling parameter (0.0 to 1.0) */\r\n  topP?: number;\r\n\r\n  /** Optimixer reasoning depth; overrides Catalox catalog default when set. */\r\n  reasoningEffort?: \"not-applicable\" | \"none\" | \"low\" | \"medium\" | \"high\";\r\n  \r\n  /** Frequency penalty (-2.0 to 2.0) */\r\n  frequencyPenalty?: number;\r\n  \r\n  /** Presence penalty (-2.0 to 2.0) */\r\n  presencePenalty?: number;\r\n  \r\n  /** Stop sequences (array of strings) */\r\n  stop?: string[];\r\n  \r\n  /** Additional provider-specific parameters */\r\n  [key: string]: any;\r\n}\r\n```\r\n\r\n### Usage Examples\r\n\r\n#### Basic Model Selection\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Your question here\",\r\n  modelConfig: {\r\n    model: \"gpt-4-turbo\"\r\n  }\r\n});\r\n```\r\n\r\n#### Full Model Configuration\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Your question here\",\r\n  modelConfig: {\r\n    model: \"gpt-4-turbo\",\r\n    temperature: 0.7,\r\n    topP: 0.9,\r\n    reasoningEffort: \"medium\",\r\n    frequencyPenalty: 0.5\r\n  }\r\n});\r\n```\r\n\r\n#### Per-node reasoning depth\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Your question here\",\r\n  jobId: \"job-1\",\r\n  taskId: \"task-1\",\r\n  agentId: \"agent-abc\",\r\n  jobTypeId: \"your-job-type-id\",\r\n  taskTypeId: \"your-task-type-id\",\r\n  modelConfig: {\r\n    model: \"gpt-4-turbo\",\r\n    reasoningEffort: \"high\", // overrides Catalox optimixerReasoningEffort for this invoke\r\n  },\r\n});\r\n```\r\n\r\n#### Provider Override\r\n\r\n```typescript\r\nconst res = await skills.runSkill({\r\n  skillKey: \"skills/professional-answer\",\r\n  input: \"Your question here\",\r\n  modelConfig: {\r\n    provider: \"anthropic\",\r\n    model: \"claude-3-opus\",\r\n    temperature: 0.3\r\n  }\r\n});\r\n```\r\n\r\n### Notes\r\n\r\n- **`modelConfig.model` / `modelId`** are **required** (concrete wire id). Missing or alias-shaped values throw before `gateway.invoke()`. See **[docs/MODEL_INVOKE_CONTRACT.md](docs/MODEL_INVOKE_CONTRACT.md)**.\r\n- **`maxTokens`** on the request are **stripped**; **Optimixer** sets the cap from Catalox catalog fields and resolved **`reasoningEffort`**.\r\n- **`reasoningEffort`** on the request overrides the Catalox catalog default for that skill key.\r\n- This package does **not** merge a global default `modelConfig` at construction time; only **`templateRendering`** has client-wide defaults for template rendering.\r\n- The gateway and provider enforce valid ranges (e.g. temperature).\r\n- **OpenRouter routing policy (`PREFER_OPENROUTER`)** is **not** applied in this package. Orchestrators (**`@exellix/ai-tasks`**, graph-engine) resolve the plan and set the wire model on `modelConfig` before `runSkill()`. See **[docs/PREFER_OPENROUTER_ROUTING.md](docs/PREFER_OPENROUTER_ROUTING.md)**.\r\n\r\n## API Reference\r\n\r\n### `ExellixSkillsClient`\r\n\r\n#### Constructor options (essential)\r\n\r\n```typescript\r\nimport type { Catalox } from \"@x12i/catalox\";\r\nimport type { TemplateRenderOptions } from \"@x12i/ai-gateway\";\r\n\r\ninterface ExellixSkillsClientOptions {\r\n  /** Required. Skill bodies are read from the native `ai-skills` catalog. */\r\n  catalox: Catalox;\r\n  /** Optional; defaults to catalog app id `ai-skills`. */\r\n  cataloxAppId?: string;\r\n\r\n  packageName?: string;\r\n  enableActivityTracking?: boolean;\r\n  /** Optional external gateway; skill templates still load from `catalox`. */\r\n  gateway?: AIGateway;\r\n\r\n  templateRendering?: TemplateRenderOptions;\r\n  autoLoadDotenv?: boolean;\r\n  testMode?: boolean;\r\n  disableActivityTrackingInTests?: boolean;\r\n}\r\n```\r\n\r\nThe built-in **`AIGateway`** has **no nx-content / GitHub skill registry**; **`GITHUB_*`** is **not** required for **`ExellixSkillsClient`** initialization.\r\n\r\n#### Methods\r\n\r\n- **`runSkill<T>(input: RunSkillRequest): Promise<RunSkillResponse<T>>`** — loads templates from Catalox, invokes gateway with inline bodies. Optional **`outputContract`** enriches **`parsed`** and the Activix activity row (see [Cost and output contract](#cost-and-output-contract-run-analysis)).\r\n- **`getSkillTemplatesForPresentation(skillKey, opts?)`** — editor-oriented markdown + metadata.\r\n- **`updateSkillTemplatesFromPresentation(skillKey, patch, opts?)`** — save edited markdown (lossless storage normalization); requires Catalox **write** context.\r\n- **`getSkillTemplateInputs(skillKey)`** — `{{token}}` discovery from stored bodies.\r\n- **`listPublishedSkills(options?)`** — Catalox rows with **`status === \"published\"`**.\r\n- **`listCatalogSkills(options?)`** — all catalog rows (any status); optional Catalox list query options.\r\n- **`upsertSkillCatalogItem(input, options?)`** — create/update catalog metadata and template bodies; **`options.ifNotExists`** enforces create-only.\r\n- **`softDeleteSkillCatalogItem(skillKey)`** — clears template and audit text and sets **`planned`**.\r\n\r\nTemplate bodies are loaded **only** through **`runSkill`** (and the same Catalox fetch used there): pass **`skillKey`**; there is no separate “peek at one section string” API.\r\n\r\n#### Diagnostic methods (Catalox catalog)\r\n\r\n- `testCatalogConnection()`, `discoverCatalogStructure()`, `diagnoseSkillInCatalog()`, `listAvailableSkills()`, `runCatalogDiagnostics()` — Catalox **`ai-skills`** catalog reachability and discovery.\r\n\r\nFor **`{{token}}`** discovery on stored bodies without invoking the LLM, use **`getSkillTemplateInputs(skillKey)`** (Catalox read + parse).\r\n\r\n## FlexMD 2.0 Format\r\n\r\nPrimary skills are designed around **structured markdown** (headings and/or Flex-MD style markers). A common explicit shape is:\r\n\r\n```\r\n[[professional-answer]]\r\n\r\n@payload:shortAnswer\r\nBrief answer here...\r\n\r\n@payload:fullAnswer\r\nDetailed answer here...\r\n\r\n@payload:assumptions\r\nList of assumptions...\r\n```\r\n\r\nThe gateway normalizes responses with its own Flex-MD extraction. When it does not return parsed fields, this SDK uses **`parseFlexMd`** (`src/utils/flex-md-parser.ts`), which prefers the **`flex-md`** package via ESM `import()` and falls back to parsing `[[frame:...]]` + `@payload:` blocks.\r\n\r\nWhen you pass **`outputContract`**, the SDK also maps markdown `### Section` headings to camelCase keys (e.g. `### Full Answer` → `fullAnswer`) so professional-answer templates that use headings still populate **`parsed`** even without `@payload:` markers. See **[Cost and output contract](#cost-and-output-contract-run-analysis)**.\r\n\r\nDetails and caveats: **[docs/FLEX_MD_AND_TESTING.md](docs/FLEX_MD_AND_TESTING.md)**.\r\n\r\n## Requirements\r\n\r\n- **Node.js 20+** (see `package.json` **`engines`**; aligns with **`@x12i/catalox`**)\r\n- **`@x12i/catalox`** **≥ 4** and **Firebase** access for the **`ai-skills`** native catalog (see **`createCataloxFromEnv`** and [Environment (Firebase & Catalox v4)](#environment-firebase--catalox-v4))\r\n- **`@x12i/ai-gateway`** **^10.4.4** (see `package.json`; shares **`@x12i/activix` ^9.0** with this package)\r\n- **`@x12i/ai-profiles`** **^3.0.0** (profile/choice → wire model resolution)\r\n- **`@x12i/activix`** **^8.6.0** (direct dependency for activity-row enrichment types/patches)\r\n- At least one LLM provider key when using the default constructed gateway\r\n\r\n**Downstream orchestrators:** `@exellix/ai-tasks` **≥ 8.7.0** depends on this package (**≥ 6.4.0**) and **`@exellix/xynthesis` ≥ 4.5.0** for the full PRE/MAIN/POST stack — see **[docs/UPGRADING_AI_GATEWAY_10.md](docs/UPGRADING_AI_GATEWAY_10.md)**.\r\n\r\n## Related runtime packages\r\n\r\nTask and graph orchestration live in sibling packages, not in **`@exellix/ai-skills`**:\r\n\r\n- **`@exellix/ai-tasks`** — task orchestration and task-level runtime wiring (**≥ 8.7.0** pins **`@exellix/ai-skills` ≥ 6.4.0** + **`@exellix/xynthesis` ≥ 4.5.0**; gateway 10.x billing and invoke rules apply to MAIN via this package)\r\n- **`@exellix/graph-engine`** — graph execution and graph-level runtime wiring; forward each node’s **`inputs.outputContract`** on **`runSkill`** when Run Analysis or downstream validation require structured **`parsed`** fields\r\n- **`@exellix/exellix-runtime`** — root runtime composition (for example loading composed `runtimeObjects` for debug tooling)\r\n\r\nOlder scopes such as **`@woroces/*`**, **`worox`**, **`worex`**, and graph packages named **`worox-graph`** / **`worex-graphs`** are not used here; there are **no** intentional aliases or compatibility shims for those names in this repository.\r\n\r\n## License\r\n\r\nISC\r\n","readmeFilename":"README.md"}