afv-library/skills/investigating-agentforce-d360/assets/dc/feedback.sql

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@W-22707610 feat: add investigating-agentforce-d360 skill 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.
2026-05-28 18:57:52 +08:00
-- User feedback on generations — one row per feedback event.
-- DMO: GenAIFeedback__dlm
--
-- Placeholders (substituted by scripts/dc.py.load_sql):
-- WHERE_CLAUSE — the filter expression, no "WHERE" keyword
-- ORDER_BY — full "ORDER BY <col>" or empty string
--
-- Captures thumbs-up/thumbs-down (and richer `action__c`) that a user gave
-- a specific generation. Joined via `generationId__c = Generation.generationId__c`.
-- `feedbackId__c` is the PK that GenAIFeedbackDetail rows point at.
--
-- NOTE: No `ssot__` prefix — fields end in `__c` directly.
SELECT
feedbackId__c,
generationId__c,
generationUpdateId__c,
generationGroupId__c,
userId__c,
feedback__c,
action__c,
source__c,
feature__c,
appType__c,
timestamp__c,
orgId__c,
cloud__c
FROM GenAIFeedback__dlm
WHERE {{WHERE_CLAUSE}}
{{ORDER_BY}};
-- ============================================================================
-- EXAMPLE WHERE clauses (pass via where_clause=)
-- ============================================================================
-- Feedback for a set of generations (typical session trace path)
-- WHERE → generationId__c IN ('<gen_id1>','<gen_id2>',...)
-- ORDER BY → ORDER BY timestamp__c
-- Feedback by a specific user in a time window
-- WHERE → userId__c = '<user_id>'
-- AND timestamp__c >= '<iso_cutoff>'
-- Only thumbs-down
-- WHERE → generationId__c IN ('<gen_id1>','<gen_id2>')
-- AND feedback__c = 'DOWN'