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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.
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
"""Shared per-session JSON writer.
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Every Data Cloud artifact lands under the nested layout:
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DATA_ROOT/<org_id_15>/<agent_api_name>__<agent_version>/<session_id>/<source>.<name>.json
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One save, one shape, one directory convention — imported by every caller.
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**Security contract:**
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All four path segments (``org_id_15``, ``agent_api_name``, ``agent_version``,
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``session_id``) flow through ``paths.session_dir(...)``, which validates
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each via fs_guard (regex) before the join. A ``..`` or ``/`` in any
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segment raises ``paths.PathValidationError`` — direct
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``DATA_ROOT / session_id`` composition is unreachable from this module.
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Source-prefix convention:
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Flat directory, prefix filenames with provenance so an `ls` on the
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per-session dir tells you where each artifact came from:
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dc.sessions.json — from Data Cloud
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dc.interactions.json
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Baking the prefix into the API (vs. leaving it to filename discipline)
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makes it impossible to forget — callers pass ``source`` + ``name``,
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never a raw filename.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from config import paths
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def save(
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data: list[dict] | dict,
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org_id_15: str,
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agent_api_name: str,
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agent_version: str,
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session_id: str,
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source: str,
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name: str,
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) -> Path:
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"""Write JSON under the nested session dir. Returns the target path.
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Signature:
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save(data, org_id_15, agent_api_name, agent_version, session_id,
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source, name) -> Path
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``source`` is the leading filename segment for provenance — callers
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pass ``"dc"``. ``name`` is the bare artifact name, no extension —
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``.json`` is appended.
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Raises ``paths.PathValidationError`` if any of the four identity
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segments fails validation.
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"""
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# paths.session_dir() validates org_id_15, agent_api_name, agent_version
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# (via fs_guard) and session_id (via SESSION_ID_RE.fullmatch). Any bad
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# input raises PathValidationError before we touch the filesystem.
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target = paths.session_dir(
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org_id_15, agent_api_name, agent_version, session_id
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)
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target.mkdir(parents=True, exist_ok=True)
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path = target / f"{source}.{name}.json"
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path.write_text(json.dumps(data, indent=2, default=str) + "\n")
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_write_breadcrumb(org_id_15, agent_api_name, agent_version, session_id)
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return path
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def _write_breadcrumb(
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org_id_15: str,
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agent_api_name: str,
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agent_version: str,
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session_id: str,
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) -> None:
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"""Write ``<org>/_sessions/<sid>.link`` pointing at the session dir.
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Plain text, no symlink (Windows-safe). Content is the relative path
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``../<agent>__<ver>/<sid>\\n``. Idempotent — overwriting with the
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same content is a no-op semantically. Silent on failure; breadcrumbs
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are best-effort (a missing breadcrumb doesn't break the write,
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only handoff-session discovery).
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"""
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try:
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link_dir = paths.DATA_ROOT / org_id_15 / "_sessions"
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link_dir.mkdir(parents=True, exist_ok=True)
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link_path = link_dir / f"{session_id}.link"
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link_path.write_text(
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f"../{agent_api_name}__{agent_version}/{session_id}\n"
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)
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except OSError:
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pass
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