sshvarzman-salesforce/claude-skills

sf-agent-api-integration-patterns

Agent API integration architecture — website chat, headless automation, platform connectors, and agent-to-agent patterns.

Voir la source

Mode d’emploi

  1. Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
  2. Consultez le document original pour les outils, identifiants et environnements requis.
  3. Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/sshvarzman-salesforce/claude-skills --skill sf-agent-api-integration-patterns

Avant de commencer

  • L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
  • Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

Agent API Integration Patterns

Use This Skill When

  • Choosing an integration pattern for the Agent API (website, headless, connector, agent-to-agent).
  • Structuring a reusable client: token management, session lifecycle, message loop, streaming, retries.
  • Setting the security and resilience posture before building.

Integration Patterns

PatternDescriptionDeliveryNotes
Website chatEmbed agent chat on a site/appStreamingToken brokered server-side; browser never sees the secret
Headless automationAutomate functionality without UISyncServer-to-server; ideal for jobs/workflows
Platform connectorConnect to Slack, Teams, custom workflowsSync or streamingStandardized REST calls per platform SDK
Agent-to-agentOne agent invokes anotherSyncBuild an agentic ecosystem; watch credit consumption

Reference Client Architecture

Structure any integration around four layers:

  1. Token service — mints and caches the client-credentials token, refreshes before expiry, and keeps the secret server-side.
  2. Session manager — starts sessions (externalSessionKey per conversation), tracks sessionId + sequenceId, and ends sessions.
  3. Message loop — serializes turns per session (one request in flight), builds the message body, attaches variables.
  4. Response handler — parses sync Inform or consumes the SSE stream; renders citations; captures feedbackId.
[Client UI / Job] → [Message loop] → [Session manager] → [Token service] → Agent API
                         ↑                                   (cache token)
                   [Response handler] ← SSE / JSON ←─────────────┘

Browser-safe pattern (website)

  • Never expose the consumer secret or token to the browser.
  • Front-end talks to your backend; backend brokers the token and proxies Agent API calls.
  • Stream from backend to browser (SSE/WebSocket) if real-time rendering is needed.

Resilience Checklist

  • Token refresh: refresh on 401 and proactively before expiry.
  • Concurrency: queue turns per session; a concurrent request returns 423.
  • Timeout: the API times out at 120s → surfaced as 500; set client timeouts and a user-facing fallback.
  • Retries: retry idempotent failures (network, 500 transient) with backoff; do not blindly resend a message that may have partially processed.
  • Session cleanup: DELETE sessions on conversation end; reap abandoned sessions.
  • Government Cloud: switch base host to api.gov.salesforce.com.

Guardrails

  • Server-side secret/token custody only — treat the browser as hostile.
  • One in-flight request per session; serialize turns.
  • Use the My Domain URL for instanceConfig.endpoint, not the Lightning URL.
  • Agent API usage consumes credits (see Generative AI Usage and Billing) — budget agent-to-agent chains carefully.
  • Not supported for "Agentforce (Default)" agents — confirm agent type before integrating.
  • Log traceId, sessionId, and externalSessionKey for every turn to support debugging and audit.

Deliverables

  • Chosen integration pattern with rationale (surface, sync vs streaming, credit impact).
  • A layered client design (token / session / message / response) with concurrency and retry policy.
  • A security posture doc (secret custody, token brokering, PII handling).
  • A resilience matrix covering 401 / 423 / 500 / timeout / session cleanup.

Additional Resources

  • Setup and tokens: sf-agent-api-setup
  • Session and messaging mechanics: sf-agent-api-session-lifecycle, sf-agent-api-messaging
  • Context passing: sf-agent-api-variables
  • Error handling: sf-agent-api-troubleshooting
  • Pre-ship review: sf-agent-api-review
  • Official docs: Agent API Developer Guide, Considerations

Tâches connexes

du même dépôt

Autres Skills

Tous les Skills
sshvarzman-salesforce
Communauté

activating-datacloud

Salesforce Data Cloud Act phase. Use this skill when the user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. 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 segmenting-datacloud), data retrieval/search work (use retrieving-datacloud), or STDM/session tracing (use observing-agentforce).

installations
1
GitHub Stars
0
Mis à jour
15 oct.
sshvarzman-salesforce
Communauté

agentforce-skills-research

Research Agentforce configuration patterns — topics, actions, instructions, CLTs, knowledge, planner behavior. Searches Salesforce docs, Trailhead, internal repos (git.soma), Slack channels, and Google Docs to find working examples, known issues, and best practices.

installations
1
GitHub Stars
0
Mis à jour
15 oct.
sshvarzman-salesforce
Communauté

harmonizing-datacloud

Salesforce Data Cloud Harmonize phase. Use this skill when the user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. 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 preparing-datacloud), segments/insights (use segmenting-datacloud), retrieval/search (use retrieving-datacloud), or STDM/sessio

installations
1
GitHub Stars
0
Mis à jour
15 oct.
sshvarzman-salesforce
Communauté

preparing-datacloud

Salesforce Data Cloud Prepare phase. Use this skill when the user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations. 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 connecting-datacloud), DMOs and identity resolution (use harmonizing-datacloud), or query/search work (use retrieving-datacloud)

installations
1
GitHub Stars
0
Mis à jour
15 oct.