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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.
48 lines
1.5 KiB
SQL
48 lines
1.5 KiB
SQL
-- User feedback on generations — one row per feedback event.
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-- DMO: GenAIFeedback__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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-- Captures thumbs-up/thumbs-down (and richer `action__c`) that a user gave
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-- a specific generation. Joined via `generationId__c = Generation.generationId__c`.
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-- `feedbackId__c` is the PK that GenAIFeedbackDetail rows point at.
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--
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-- NOTE: No `ssot__` prefix — fields end in `__c` directly.
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SELECT
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feedbackId__c,
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generationId__c,
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generationUpdateId__c,
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generationGroupId__c,
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userId__c,
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feedback__c,
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action__c,
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source__c,
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feature__c,
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appType__c,
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timestamp__c,
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orgId__c,
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cloud__c
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FROM GenAIFeedback__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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-- Feedback for a set of generations (typical session trace path)
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-- WHERE → generationId__c IN ('<gen_id1>','<gen_id2>',...)
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-- ORDER BY → ORDER BY timestamp__c
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-- Feedback by a specific user in a time window
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-- WHERE → userId__c = '<user_id>'
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-- AND timestamp__c >= '<iso_cutoff>'
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-- Only thumbs-down
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-- WHERE → generationId__c IN ('<gen_id1>','<gen_id2>')
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-- AND feedback__c = 'DOWN'
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