Extracción y análisis de trazas de sesiones de Agentforce. ACTIVAR cuando: el usuario extrae datos STDM de Data Cloud, analiza trazas de sesiones de agentes, depura conversaciones de agentes a través de telemetría, o trabaja con archivos .parquet de Agentforce. NO ACTIVAR cuando: se prueban agentes (usar sf-ai-agentforce-testing), se revisan registros de depuración de Apex (usar sf-debug), o se construyen agentes (usar sf-ai-agentforce).
Esta skill permite extraer y analizar trazas de sesiones de Agentforce, facilitando la depuración y comprensión del comportamiento de los agentes mediante telemetría. Es especialmente útil cuando se trabaja con datos STDM de Data Cloud o archivos .parquet generados por Agentforce. La skill está diseñada para plataformas compatibles con el ecosistema Salesforce y entornos de desarrollo de agentes de IA, ofreciendo una integración directa para la observabilidad. Proporciona una capa de análisis que permite a desarrolladores y administradores identificar problemas de rendimiento, errores de conversación y patrones de uso en las interacciones de los agentes. Se activa exclusivamente en contextos de extracción y análisis de trazas, evitando interferencias con procesos de prueba o depuración de otros componentes. Su implementación se basa en la documentación SKILL.md, que detalla los casos de activación y las instrucciones precisas para su uso en entornos de desarrollo.
npx skills add https://github.com/jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observabilityInstalled? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Extracción y análisis de trazas de sesiones de Agentforce. ACTIVAR cuando: el usuario extrae datos STDM de Data Cloud, analiza trazas de sesiones de agentes, depura conversaciones de agentes a través de telemetría, o trabaja con archivos .parquet de Agentforce. NO ACTIVAR cuando: se prueban agentes (usar sf-ai-agentforce-testing), se revisan registros de depuración de Apex (usar sf-debug), o se construyen agentes (usar sf-ai-agentforce).
This repo contains 20 individual skills — each has its own dedicated page.
Agentforce Builder metadata path for Builder-managed topics/actions, Prompt Builder templates, GenAiFunction/GenAiPlugin, Models API, and custom Lightning types. TRIGGER when: user maintains or configures Builder metadata agents, creates topics/actions, works with Prompt Builder templates, or touches .genAiFunction, .genAiPlugin, or .genAiPromptTemplate metadata XML files. DO NOT TRIGGER when: Agent Script DSL .agent files (use sf-ai-agentscript), agent testing (use sf-ai-agentforce-testing), or persona design (use sf-ai-agentforce-persona).
Deep persona design for Agentforce agents with 50-point scoring. TRIGGER when: user designs agent personas, defines agent personality/identity, creates persona documents, encodes persona into Agentforce Builder fields or Agent Script, translates brand guidelines to agent voice, or asks about agent tone/voice/register. DO NOT TRIGGER when: building agent metadata (use sf-ai-agentforce), testing agents (use sf-ai-agentforce-testing), or Agent Script DSL (use sf-ai-agentscript).
Agentforce agent testing with dual-track workflow and 100-point scoring. TRIGGER when: user tests Agentforce agents, runs sf agent test commands, creates test specs, validates topic routing, or analyzes agent test coverage. DO NOT TRIGGER when: Apex unit tests (use sf-testing), building agents (use sf-ai-agentforce), or Agent Script DSL (use sf-ai-agentscript).
Agent Script DSL for deterministic Agentforce agents. TRIGGER when: user writes or edits .agent files, builds FSM-based agents, uses Agent Script CLI (sf agent generate authoring-bundle, sf agent validate authoring-bundle, sf agent preview, sf agent publish authoring-bundle, sf agent activate), or asks about deterministic agent patterns, slot filling, or instruction resolution. DO NOT TRIGGER when: Builder metadata work (use sf-ai-agentforce), agent testing (use sf-ai-agentforce-testing), or persona design (use sf-ai-agentforce-persona).
Generates and reviews Salesforce Apex code with 150-point scoring. TRIGGER when: user writes, reviews, or fixes Apex classes, triggers, test classes, batch/queueable/schedulable jobs, or touches .cls/.trigger files. DO NOT TRIGGER when: LWC JavaScript (use sf-lwc), Flow XML (use sf-flow), SOQL-only queries (use sf-soql), or non-Salesforce code.
Salesforce Connected Apps and OAuth configuration with 120-point scoring. TRIGGER when: user configures OAuth flows, JWT bearer auth, Connected Apps, or touches .connectedApp-meta.xml / .eca-meta.xml files. DO NOT TRIGGER when: Named Credentials for callouts (use sf-integration), permission policies (use sf-permissions), or API endpoint code (use sf-apex).
Salesforce data operations with 130-point scoring. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, or needs data factory patterns for Apex tests. DO NOT TRIGGER when: SOQL query writing only (use sf-soql), Apex test execution (use sf-testing), or metadata deployment (use sf-deploy).
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
Salesforce Data Cloud Act phase. TRIGGER when: user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. DO NOT TRIGGER when: the task is segment creation (use sf-datacloud-segment), data retrieval/search work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Salesforce Data Cloud Connect phase. TRIGGER when: user manages Data Cloud connections, connectors, connector metadata, tests a connection, browses source objects or databases, or sets up a new source system. DO NOT TRIGGER when: the task is about data streams or DLOs (use sf-datacloud-prepare), DMOs or identity resolution (use sf-datacloud-harmonize), retrieval/search (use sf-datacloud-retrieve), or STDM telemetry (use sf-ai-agentforce-observability).
Salesforce Data Cloud Harmonize phase. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use sf-datacloud-prepare), segments/insights (use sf-datacloud-segment), retrieval/search (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Salesforce Data Cloud Prepare phase. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use sf-datacloud-connect), DMOs and identity resolution (use sf-datacloud-harmonize), or query/search work (use sf-datacloud-retrieve).
Salesforce Data Cloud Retrieve phase. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use sf-soql), segment creation or calculated insight design (use sf-datacloud-segment), or STDM/session tracing/parquet analysis (use sf-ai-agentforce-observability).
Salesforce Data Cloud Segment phase. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use sf-datacloud-harmonize), activation work (use sf-datacloud-act), query/search-index work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Salesforce debug log analysis and troubleshooting with 100-point scoring. TRIGGER when: user analyzes debug logs, hits governor limits, reads stack traces, or touches .log files from Salesforce orgs. DO NOT TRIGGER when: running Apex tests (use sf-testing), fixing Apex code (use sf-apex), or Agentforce session tracing (use sf-ai-agentforce-observability).
Salesforce DevOps automation using sf CLI v2. TRIGGER when: user deploys metadata, creates/manages scratch orgs or sandboxes, sets up CI/CD pipelines, or troubleshoots deployment errors with sf project deploy. DO NOT TRIGGER when: writing Apex/LWC code (use sf-apex/sf-lwc), creating metadata XML (use sf-metadata), or querying org data (use sf-data).
Salesforce architecture diagrams using Mermaid with ASCII fallback. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use sf-diagram-nanobananapro), or asks about non-Salesforce systems.
AI-powered image generation for Salesforce visuals via Nano Banana Pro. TRIGGER when: user asks for PNG/SVG output, UI mockups, wireframes, visual ERDs, or says "generate image" / "create mockup". DO NOT TRIGGER when: text-based Mermaid diagrams (use sf-diagram-mermaid), or non-visual documentation tasks.
Official Salesforce documentation retrieval guidance. Use when you need authoritative Salesforce docs from developer.salesforce.com or help.salesforce.com, especially when pages are JS-heavy, shell-rendered, or hard to extract with naive fetching.
Creates and validates Salesforce Flows with 110-point scoring. TRIGGER when: user builds or edits record-triggered, screen, autolaunched, or scheduled flows, or touches .flow-meta.xml files. DO NOT TRIGGER when: Apex automation (use sf-apex), process builder migration questions only, or non-Flow declarative config (use sf-metadata).
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.