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20ae436442
Data Cloud 360° view of a single Agentforce session — DC-only, zero
Splunk dependency. Pulls 24 STDM + GenAI DMOs via the Data Cloud Query
REST API, assembles a hierarchical session tree (Interaction → Step →
Generation → GatewayRequest), and renders a human-readable markdown
summary with transcript + per-turn topic/action invocations + LLM
generations + tool calls + audit chain.
Migrated as a standalone Apache-2.0 skill from an internal hub plugin —
self-contained, no sibling-skill or plugin dependencies.
What this skill answers:
- "Trace session <uuid>" / "Summarize what happened in <0Mw…>"
- "Find escalated sessions today on Messaging in <org>"
- Session discovery by time / agent / channel / outcome / conversation
text when the user has no session id
What it does NOT answer (use a different surface):
- Design-time architecture — use investigating-agentforce-architecture
- Runtime planner availability — DC alone can't tell you which
topic/action was eligible for the classifier on a given turn
Skill layout:
- 8 Python pipeline modules (fetch_dc, assemble_dc, render_dc,
discover_sessions, resolve_session, dc, storage, config)
- 4 _shared helpers (paths, fs_guard, sql, __init__) with skill-scoped
DATA_ROOT (~/.claude/data/investigating-agentforce-d360/)
- 26 SQL templates under assets/dc/
- 27 test files (367 tests + 18 subtests, 100% passing)
- 3 reference docs (artifacts.md, dc_dmo_fields.md,
dc_pipeline_contract.md)
- SKILL.md (sf-skills frontmatter, license: Apache-2.0,
metadata.version: "1.0")
- README.md (external-facing quick-start)
- tools/grant_allowlist.py (idempotent first-run permission grant)
- tools/archive_data_dir.sh (opt-in stop-hook tarballer)
Quality gates:
- pytest scripts/tests/: 367 passed + 18 subtests, 0 failures
- npm run validate:skills: 62 of 62 skill(s) checked, 0 errors
- Live end-to-end runs against 3 real Salesforce sessions exercising
both the full-tree and STDM-lag gateway-direct render branches
- 4 independent code-review rounds (correctness, security, markdown,
architecture-critic) — all findings addressed
Customer-data hygiene: no live tenant ids, no internal sprint markers,
no hub/sibling-skill references. Synthetic fixtures look obviously
synthetic (`019dface-…` UUIDs, `0MwTESTMSG…` MessagingSession ids,
`00DTESTORG…` org ids, `MyAgent` placeholder agent name).
Sibling skill: investigating-agentforce-architecture (PR #278) — same
migration pattern, design-time metadata; complementary scope.
35 lines
1.4 KiB
SQL
35 lines
1.4 KiB
SQL
-- Moment ↔ Interaction junction — which turns belong to a moment.
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-- DMO: ssot__AiAgentMomentInteraction__dlm
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--
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-- Placeholders (substituted by scripts/dc.py.load_sql):
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-- WHERE_CLAUSE — the filter expression, no "WHERE" keyword
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-- ORDER_BY — full "ORDER BY <col>" or empty string
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--
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-- Agent Optimization add-on DMO. Provisioned only when Agent Optimization
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-- is enabled. Junction between AiAgentMoment and AIAgentInteraction.
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-- Observed live: one Moment per Interaction (N:1 direction). The junction
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-- schema supports true many-to-many; the assembler emits Moment.interaction_ids[]
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-- back-refs to preserve the schema-correct shape even when live data is 1:N.
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SELECT
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ssot__Id__c,
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ssot__AiAgentMomentId__c,
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ssot__AiAgentInteractionId__c,
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ssot__StartTimestamp__c,
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ssot__InternalOrganizationId__c
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FROM ssot__AiAgentMomentInteraction__dlm
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WHERE {{WHERE_CLAUSE}}
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{{ORDER_BY}};
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-- ============================================================================
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-- EXAMPLE WHERE clauses (pass via where_clause=)
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-- ============================================================================
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-- Junction rows for a set of moments
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-- WHERE → ssot__AiAgentMomentId__c IN ('<mom_id1>','<mom_id2>',...)
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-- ORDER BY → ORDER BY ssot__StartTimestamp__c
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-- Junction rows for a set of interactions (reverse lookup)
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-- WHERE → ssot__AiAgentInteractionId__c IN ('<int_id1>','<int_id2>')
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