forcedotcom/sf-skills is a collection of reusable instructions that help AI coding agents build and work with Salesforce applications and platform features. Developers use it for Agentforce agents, Lightning apps, Flow, Apex, SOQL, Lightning Web Components, and related Salesforce configuration. The catalogue entries are the repository’s skills, plugins, commands, MCP servers, and agents for AI tools that support skills.
Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add forcedotcom/sf-skills/plugin install agentforce-adlcWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/forcedotcom/sf-skills/agentforce-d360-analyze)<a href="https://agentmods.dev/skills/forcedotcom/sf-skills/agentforce-d360-analyze"><img src="https://agentmods.dev/badge/skills/forcedotcom/sf-skills/agentforce-d360-analyze/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/forcedotcom/sf-skills/agentforce-d360-analyze"><img src="https://agentmods.dev/badge/skills/forcedotcom/sf-skills/agentforce-d360-analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 25 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.03342 |
| Opus 5.5 | $0.00052 | $0.01337 |
| Sonnet 5.5 | $0.00026 | $0.00668 |
| Haiku 4.5 | $0.00013 | $0.00334 |
Grade B, and why
agentforce-d360-analyze scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
When invoked with no session id AND no discovery criteria, print this block **verbatim** — do not paraphrase, do not pre-run any script. Trigger condition: the input is empty OR contains no session-id shape (neither a UU How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agentforce-d360-analyze — Data Cloud 360° session view
Hierarchical session reconstruction from Data Cloud STDM + GenAI DMOs for one Agentforce session. Three stages — fetch → assemble → render. Typical wall-clock: ~10–30s for a ~15-turn session.
The pipeline is DC-only: it reads runtime audit rows that Data Cloud has materialized. It is not a runtime-availability tool — see "DC-only blind spot" below for what this skill cannot answer.
If the user hasn't given enough to proceed
When invoked with no session id AND no discovery criteria, print this block verbatim — do not paraphrase, do not pre-run any script. Trigger condition: the input is empty OR contains no session-id shape (neither a UUID nor a 0Mw… messaging id) AND no discovery expression (no time phrase / --agent / --channel / --outcome / --grep / verbs like "find" / "list").
Which session should I pull from Data Cloud, and in which org?
I need:
- Session id — either an Agent Session UUID (
019db7f6-…) or a MessagingSession id (0Mw…, 15/18 chars).
- No session id? — Tell me what you remember and I'll find it: how recent (e.g. "last 2 hours", "today", a date), which agent, which channel (Messaging / Builder / Voice), how it ended (escalated, user ended, transferred, timed out), or a phrase from the conversation. I'll show matching sessions as a numbered list — you pick one, I pull it.
- Org alias — for
sfCLI auth (the alias you configured withsf org login).Artifacts land in
~/.vibe/data/agentforce-d360-analyze/<org_id15>/<agent>__<ver>/<session_id>/(override per-script with--data-dir <path>).
Session id forms — UUID or MessagingSession id
Both forms are accepted on --session:
| Form | Example | Resolution |
|---|---|---|
| Agent Session UUID | 019dface-0000-7000-8000-000000000002 |
Pass-through |
MessagingSession id (0Mw prefix) |
0MwTESTMSG12345AAA |
Resolved via resolve_session.py — live DC lookup on first fetch, disk-first thereafter |
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/dc/app_generation.sql 1.9 KB
- assets/dc/content_category.sql 1.5 KB
- assets/dc/content_quality.sql 1.3 KB
- assets/dc/discover_sessions.sql 2.3 KB
- assets/dc/feedback_details.sql 1.3 KB
- assets/dc/feedback.sql 1.5 KB
- assets/dc/gateway_records.sql 1.6 KB
- assets/dc/gateway_request_llm.sql 1.6 KB
- assets/dc/gateway_request_metadata.sql 1.5 KB
- assets/dc/gateway_request_tags.sql 1.4 KB
- assets/dc/gateway_requests.sql 3.4 KB
- assets/dc/gateway_responses.sql 1.7 KB
- assets/dc/generations.sql 1.9 KB
- assets/dc/interactions.sql 2.0 KB
- assets/dc/messages.sql 2.1 KB
- assets/dc/messaging_session.sql 1.7 KB
- assets/dc/moment_interactions.sql 1.4 KB
- assets/dc/moments.sql 1.5 KB
- assets/dc/participants.sql 1.8 KB
- assets/dc/sessions.sql 3.0 KB
- assets/dc/steps.sql 2.6 KB
- assets/dc/tag_associations.sql 1.7 KB
- assets/dc/tag_definition_associations.sql 1.4 KB
- assets/dc/tag_definitions.sql 1.8 KB
- assets/dc/tags.sql 1.3 KB
- assets/dc/telemetry_spans.sql 2.1 KB
- README.md 6.2 KB
- references/artifacts.md 2.7 KB
- references/dc_dmo_fields.md 42 KB
- references/dc_pipeline_contract.md 26 KB
- scripts/_shared/__init__.py 122 B runs code
- scripts/_shared/cli_override.py 3.3 KB runs code
- scripts/_shared/fs_guard.py 13 KB runs code
- scripts/_shared/paths.py 5.8 KB runs code
- scripts/_shared/runtime.py 2.0 KB runs code
- scripts/_shared/sql.py 548 B runs code
- scripts/assemble_dc.py 70 KB runs code
- scripts/config.py 1.8 KB runs code
- scripts/dc.py 14 KB runs code
- scripts/discover_sessions.py 21 KB runs code
- scripts/fetch_dc.py 45 KB runs code
- scripts/render_dc.py 70 KB runs code
- scripts/resolve_session.py 11 KB runs code
- scripts/storage.py 3.1 KB runs code
- scripts/tests/__init__.py 0 B runs code
- scripts/tests/_bootstrap.py 483 B runs code
- scripts/tests/fixtures/__init__.py 0 B runs code
- scripts/tests/fixtures/synthetic_session.py 15 KB runs code
- scripts/tests/test_assemble_dc_bootstrap_and_mode.py 4.2 KB runs code
- scripts/tests/test_assemble_dc_gateway_direct_integration.py 6.4 KB runs code
- scripts/tests/test_assemble_dc_gateway_direct.py 8.3 KB runs code
- scripts/tests/test_assemble_dc_helpers.py 10 KB runs code
- scripts/tests/test_assemble_dc_integration.py 9.3 KB runs code
- scripts/tests/test_dc_and_resolve_session.py 27 KB runs code
- scripts/tests/test_default_target_org.py 19 KB runs code
- scripts/tests/test_discover_sessions_grep_ci.py 8.2 KB runs code
- scripts/tests/test_discover_sessions.py 19 KB runs code
- scripts/tests/test_fetch_dc_helpers.py 11 KB runs code
- scripts/tests/test_fetch_dc_identity.py 22 KB runs code
- scripts/tests/test_fetch_dc_main.py 9.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago Changed · +18 lines d9e5642b8743
- 1mo ago First seen · 173 lines · 129 tokens per session scan B 8a1daa887578
agentforce-d360-analyze is a skill published in the GitHub repository forcedotcom/sf-skills (1,097 stars, last pushed today), licensed Apache-2.0. It adds 129 tokens to every session and 3,342 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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