afv-library/skills/agentforce-d360-analyze

agentforce-d360-analyze

Data Cloud 360° view of a single Agentforce session. Pulls 24 STDM + GenAI DMOs from Salesforce Data Cloud, assembles a hierarchical session tree (Interaction → Step → Generation → GatewayRequest), and renders a human-readable markdown summary.

This skill is DC-only — it reads runtime audit data that Salesforce Data Cloud has materialized for a session. It does not call into runtime telemetry, performance services, or any Splunk / observability surface.

Input: an Agent Session UUID (019d…) or a MessagingSession id (0Mw…, 15/18 chars), and an sf CLI org alias.

Output: per-DMO JSON artifacts plus three derived files under ~/.vibe/data/agentforce-d360-analyze/<org_id15>/<agent>__<version>/<session_id>/ (default; override per-script with --data-dir <path>):

• dc.<name>.json — 24 raw DMO results (one per query in the waterfall)
• dc._session_manifest.json — per-DMO row counts, classified session_shape, and empty-by-design reasons
• dc._session_tree.json — hierarchical join (the primary artifact; the summary is rendered from this)
• dc._session_summary.md — human-readable summary, up to 11 sections

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Runtime budget

~10–30s typical on a 15-turn session. The 5-wave fetch waterfall fans out 24 queries; later waves depend on ids harvested from earlier waves, so wave-to-wave is sequential, but each wave's queries run concurrently within the wave.

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Prerequisites

Tool	Why
sf CLI (authenticated against the target org)	Shells sf org display --target-org <alias> --json for the Data Cloud Query REST API access token
Data Cloud enabled on the target org	Required — the STDM + GenAI DMOs must have materialized for the session
Python 3.10+	pathlib, dataclasses, | union types

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Usage

Invoked conversationally through whatever skill-aware runtime hosts it. Example prompts:

User says	Skill does
trace session 019dface-... in my-org	Run the 3-stage pipeline: fetch → assemble → render
summarize what happened in 0MwTESTMSG12345AAA	Resolve the messaging id → UUID, then run the pipeline
find escalated sessions today on Messaging in my-org	Run discover_sessions.py, print a numbered picker, user picks one, then run the pipeline
walk me through this session	Same as trace — the rendered summary reads top-to-bottom

See SKILL.md for the full TRIGGER conditions, flag table, and the "DC-only blind spot" guidance.

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Pipeline

Three stages, each independently runnable:

fetch_dc.py     →  24 dc.<name>.json + dc._session_manifest.json   (DC Query REST waterfall)
assemble_dc.py  →  dc._session_tree.json                           (in-memory hierarchical join)
render_dc.py    →  dc._session_summary.md                          (markdown rendering)

fetch_dc.py --session <sid> --org <alias> chains all three by default. Pass --no-assemble / --no-render to stop early.

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Artifacts read order

1. dc._session_summary.md — human-readable, top-to-bottom answers "what happened in this session?"
2. dc._session_tree.json — single source of truth, the hierarchical join the summary was rendered from
3. dc._session_manifest.json — open this when something looks missing in the tree (per-DMO row counts, empty-by-design reasons)
4. dc.<name>.json — raw per-DMO rows, only when the manifest reports an unexpected count

See references/artifacts.md for the full inventory.

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What this skill does NOT answer

DC alone tells you what happened — every step, every LLM call, every gateway request, in order, with timestamps. It does not tell you what could have happened but didn't:

• Which topics were eligible for the classifier on a given turn
• Which actions survived rule expressions and were actually offered to the LLM
• Why the LLM picked one topic/action over another

If the user's question is about why a particular topic or action was or wasn't used, DC-only is almost never sufficient. See "DC-only blind spot" in SKILL.md.

For design-time architecture questions (topic/action tree, flow inventory, Apex classes, prompt templates), use the sibling skill agentforce-architecture-analyze instead.

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Layout

agentforce-d360-analyze/
├── SKILL.md                               ← runtime-parsed entry point (TRIGGER / DO NOT TRIGGER, flags, prompts)
├── README.md                              ← this file
├── scripts/
│   ├── fetch_dc.py                        ← 5-wave DC fetch + chained pipeline driver
│   ├── assemble_dc.py                     ← in-memory hierarchical join → dc._session_tree.json
│   ├── render_dc.py                       ← markdown rendering → dc._session_summary.md
│   ├── discover_sessions.py               ← session picker by time / agent / channel / outcome / grep
│   ├── resolve_session.py                 ← `0Mw…` MessagingSession id → Agent Session UUID
│   ├── dc.py                              ← DC Query REST API client (load_sql, post)
│   ├── storage.py                         ← per-session JSON writer (path-validated)
│   ├── config.py                          ← shared constants + DATA_ROOT re-export
│   ├── _shared/                           ← path / SQL helpers (paths, fs_guard, sql)
│   └── tests/                             ← pytest suite (372 tests + 18 subtests)
├── references/
│   ├── artifacts.md                       ← the full per-session artifact inventory
│   ├── dc_dmo_fields.md                   ← per-DMO field reference + cross-DMO join map
│   └── dc_pipeline_contract.md            ← pipeline contract: tree shape + render-stage section list
└── assets/
    └── dc/                                ← 26 .sql templates loaded by dc.load_sql

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Authored by

Raghul Jayagopal (RJ), Salesforce ANZ FDE.

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License

Apache-2.0. See repository root LICENSE.