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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.
38 lines
1.7 KiB
SQL
38 lines
1.7 KiB
SQL
-- MessagingSession id → AI-agent session id lookup.
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-- DMO: ssot__AIAgentSession__dlm
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--
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-- Given a Salesforce MessagingSession id (0Mw... prefix, 15 or 18 chars),
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-- find every ssot__AIAgentSession__dlm row with matching
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-- RelatedMessagingSessionId. Used by scripts/resolve_session.py to map a
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-- messaging id to the canonical AI-agent session UUID that every other
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-- script keys on.
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--
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-- Placeholders (substituted by scripts/dc.py.load_sql):
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-- MSG_ID — the MessagingSession id (pre-validated by the caller:
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-- is_messaging_id() enforces the `0Mw` key prefix plus an
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-- exact 15 or 18 char length before this template is loaded.
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-- A raw-UUID or free-text id can never reach this template.)
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--
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-- Returned rows:
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-- * zero rows → caller raises SystemExit ("no messaging session found")
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-- * one row → caller returns ssot__Id__c as the UUID
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-- * many rows → caller prints every candidate with timestamps + end_type
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-- + channel and exits non-zero so the user can pick one
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-- and re-invoke with the specific UUID.
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--
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-- The `RelatedMessagingSessionId__c != 'NOT_SET'` clause is defensive —
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-- a real msg_id cannot equal the literal 'NOT_SET', but the guard lets
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-- the template be copy-pasted for other filters that might otherwise
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-- accidentally match the sentinel.
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SELECT
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ssot__Id__c,
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ssot__StartTimestamp__c,
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ssot__EndTimestamp__c,
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ssot__AiAgentSessionEndType__c,
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ssot__AiAgentChannelType__c
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FROM ssot__AIAgentSession__dlm
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WHERE ssot__RelatedMessagingSessionId__c = '{{MSG_ID}}'
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AND ssot__RelatedMessagingSessionId__c != 'NOT_SET'
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ORDER BY ssot__StartTimestamp__c DESC;
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