AwesomeSalesforceSkills

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AwesomeSalesforceSkills

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Open-source Salesforce knowledge layer for AI coding assistants. 982+ source-grounded skills, 75 agents, 38-tool MCP server with live-org metadata. pip install sfskills-mcp.

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# SfSkills — Salesforce AI Skill Library

Make your AI coding assistant behave like a senior Salesforce practitioner on
the task in front of it: knowing the platform's non-obvious failure modes,
refusing the specific wrong code an LLM reliably produces, grounding every
claim in official Salesforce documentation, and — through the MCP server —
asking your actual org whether the thing already exists.

[![Validate](https://github.com/PranavNagrecha/AwesomeSalesforceSkills/actions/workflows/validate.yml/badge.svg)](https://github.com/PranavNagrecha/AwesomeSalesforceSkills/actions/workflows/validate.yml)
[![PR Lint](https://github.com/PranavNagrecha/AwesomeSalesforceSkills/actions/workflows/pr-lint.yml/badge.svg)](https://github.com/PranavNagrecha/AwesomeSalesforceSkills/actions/workflows/pr-lint.yml)
[![License: Apache-2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://raw.githubusercontent.com/PranavNagrecha/AwesomeSalesforceSkills/main/LICENSE)

---

## The problem

A general-purpose model has read enormous amounts of Salesforce code, and a
lot of it is wrong in ways that only surface in production. The output
compiles, passes review, and then hits a governor limit, a mixed-DML boundary,
or a sharing rule nobody modelled. The failure mode is not that the model
lacks syntax — it is that the model has no working theory of the platform's
constraints, so it confidently generalises a test-only idiom into production
code.

## Concretely

Ask a model to create an Account and a User in one service method and it
writes this:

```apex
public class AccountService {
    public static void createAccountAndUser(String name, String email) {
        Account acc = new Account(Name = name);
        insert acc;
        System.runAs(new User(Id = UserInfo.getUserId())) {
            User u = new User(/* fields */);
            insert u;
        }
    }
}
```

With this library loaded, it writes this instead:

```apex
public class AccountService {
    public static void createAccountAndUser(String name, String email) {
        Account acc = new Account(Name = name);
        insert acc;
        UserCreationService.createUserAsync(acc.Id, email);
    }
}

public class UserCreationService {
    @future
    public static void createUserAsync(Id accountId, String email) {
        User u = new User(/* fields */);
        insert u;
    }
}
```

The rule the first version violates: `User` is a setup object and `Account` is
not, so DML against both inside one transaction throws
`MIXED_DML_OPERATION`. `System.runAs()` relaxes that restriction *in test
context only* — in production Apex it is not a fix, it is a bug that compiles.
The model reaches for it because its training data is full of test classes.
([Apex Developer Guide — sObjects That Cannot Be Used Together in DML
Operations](https://developer.salesforce.com/docs/atlas.en-us.apexcode.meta/apexcode/apex_dml_non_mix_sobjects.htm))

Every skill package in this repo ships a `references/llm-anti-patterns.md`
with entries in exactly that shape: the wrong output, why the model produces
it, the correct pattern, and a detection hint.

---

## Install

Full setup reference, with captured transcripts and every flag:
[`docs/installing.md`](./docs/installing.md).

### 1. Clone it and start asking — no build step

```bash
git clone https://github.com/PranavNagrecha/AwesomeSalesforceSkills.git
cd AwesomeSalesforceSkills
```

Open that directory in Claude Code and ask a Salesforce question. That is the
whole setup for the main path.

A clone carries everything the AI needs to find a skill: `CLAUDE.md`, the **12
router skills** under `.claude/skills/`, and the **48 run-time agent loaders**
under `.claude/agents/`. Selection is model-driven, not search-driven — Claude
reads the router descriptions, opens that router's `references/skill-index.md`
(a flat roster of one-line glosses covering all 1,027 packages), and opens the
package it picks. No index is consulted.

Two things are *not* in a clone, because both are generated:
`.claude/commands/` (the 66 slash commands) and the retrieval index under
`vector_index/`. Step 2 builds both.

**As a Claude Code plugin** — namespaced skills plus the slash commands,
without adding this repo to your project:
[`docs/installing-the-plugin.md`](./docs/installing-the-plugin.md). Read its
prerequisite note first; the marketplace manifests under `.claude-plugin/` are
not on the default branch yet, so the GitHub install path is blocked until they
land (`git ls-tree origin/main .claude-plugin/` returns nothing today).

**For Cursor, Windsurf, Aider, Augment, or Codex CLI** — run
`python3 scripts/export_skills.py --target cursor` and copy the generated
`exports/cursor/.cursor/` directory into your project root (the export writes
one subdirectory per target, so copying `exports/` wholesale puts the rules in
the wrong place).

### 2. Optional — build the local index, for CLI and MCP search

```bash
python3 -m pip install -r requirements.txt
python3 scripts/bootstrap.py
python3 scripts/search_knowledge.py "trigger recursion"
```

The only entry under `Top skills:` should be
`apex/recursive-trigger-prevention`. The number beside it is a ranking output
that moves whenever the ranker is retuned — assert the skill id, never the
score.

This builds the FTS5 index behind the *keyword-search* way of finding a skill —
`search_knowledge.py`, the MCP `search_skill` tool, and the build-time agents
that maintain the library. Skip it and `search_knowledge.py` reports `Coverage: NONE` for every
query and still exits 0, which looks like an empty library rather than a missing
index. Skipping it does **not** stop Claude from reaching a skill package
through the routers above.

Bootstrap also installs the 66 slash commands into `.claude/commands/`; restart
Claude Code afterwards, since it loads commands at session start.

Cost, per the captured first-run transcript in
[`docs/installing.md` §1](./docs/installing.md#1-one-command): about **9 s** on
a `git clone --depth 1` (macOS, Apple silicon), writing roughly **290 MB** into
the gitignored `vector_index/` — 126 MB of `chunks.jsonl` and 166 MB of
`lexical.sqlite`. Those are one machine's numbers, not a guarantee. Lexical-only
is the default because `fastembed` is commented out of `requirements.txt`.
Semantic embeddings are opt-in behind `--with-embeddings`, cost **+535 MB and
hours** of encode time, and bought 0.0pp on the curated fixtures — see
[`docs/installing.md` §4](./docs/installing.md#4-embeddings-are-opt-in) before
enabling them.

> **Use `scripts/bootstrap.py`, not `scripts/build_index.py`.**
> `build_index.py` reaches the same retrieval outcome through
> `pipelines.sync_engine.write_state`, which rewrites every registry record. On
> a fresh clone with no embedding backend installed it nulls `vector_embedding`
> across all 1,027 records, leaving **1,029 modified tracked files** you then
> have to recognise as noise and discard (`scripts/bootstrap.py:33-36`).
> Bootstrap never calls `write_state`, so `git status` is clean when it
> finishes.

### 3. Optional — let the AI read your real org

```bash
python3 -m pip install -e mcp/sfskills-mcp   # published as sfskills-mcp on PyPI
sf org login web --alias my-dev              # auth stays in the sf CLI
```

### What to expect

All **1,027 of 1,027** skill packages are structurally complete — `SKILL.md`
plus all four `references/` files, verified 2026-08-07 by walking `skills/*/*/`.

Routing is a different question, and it is honest to say it is imperfect. Which
package Claude opens is a model decision made from router descriptions and
one-line glosses, so it is probabilistic and it does miss: a 12-question
fresh-clone walkthrough on 2026-08-07 landed on the right package for 9,
half-right for 1, and wrong for 2 — both misses traced to a gloss or a router
keyword list, not to missing content. Twelve questions is a sample, not a hit
rate. If Claude opens the wrong package, name the domain ("this is a sharing
question") or run `python3 scripts/search_knowledge.py "<your question>"` after
step 2.

---

## Why you can trust the output

- **Verified against a live org.** Three re-runnable harnesses:
  `scripts/validate_probes_against_org.py` (every probe's SOQL executes),
  `scripts/smoke_test_agents.py` (structural + dependency checks on all
  active runtime agents), and `scripts/validate_skill_factuality.py` (samples
  skills and checks the field/object references actually exist). Reports land
  in `docs/validation/` — see [`docs/validation/README.md`](./docs/validation/README.md).
- **Output quality is tested, not asserted.** Golden P0 cases with assertions,
  rubrics, and reference answers live in `evals/golden/`; lint them with
  `python3 evals/scripts/run_evals.py --structure`.
- **Every claim is source-graded.** A 4-tier trust ladder — official docs beat
  Trailhead/Architects beat community blogs beat forum signal — defined in
  [`standards/source-hierarchy.md`](./standards/source-hierarchy.md) and
  enforced by the content contract in
  [`standards/skill-content-contract.md`](./standards/skill-content-contract.md).
- **Structure is machine-checked.** `python3 scripts/validate_repo.py` must
  exit 0 on every change; the full gate list is in
  [`standards/validation-gates.md`](./standards/validation-gates.md).

Honest caveat: the retrieval-quality gate is currently skipped in CI and the
golden evals do not block a merge. See
[`docs/comparison.md`](./docs/comparison.md) for the full list of weak spots.

---

## What's in it

**1027 skills · 76 agents · shared Apex/LWC/Flow templates · golden evals · live-org MCP server.**

- **Skills** (`skills/`) — 1027 structured guides. Each carries SKILL.md
  instructions, worked examples, gotchas, Well-Architected mapping, and the
  anti-pattern list shown above. Full catalog: [`docs/SKILLS.md`](./docs/SKILLS.md).
- **Shared canon** — `templates/` holds the one canonical TriggerHandler,
  ApplicationLogger, SecurityUtils, HttpClient, TestDataFactory, LWC skeleton,
  Flow fault path, and Agentforce action shell that every skill points at
  ([`templates/README.md`](./templates/README.md)). `standards/decision-trees/`
  routes automation / async / integration / sharing choices before any code
  gets written.
- **Agents** (`agents/`) — instruction files any agentic AI can follow.
  **Build-time (14)** maintain the library; **Run-time (48)** do real
  Salesforce work in your codebase or org, across four tiers —
  Developer + architecture tier (16), Admin accelerators — Tier 1 (14),
  Strategic — Tier 2 (7), Vertical + governance — Tier 3 (11). Contract:
  [`agents/_shared/AGENT_CONTRACT.md`](./agents/_shared/AGENT_CONTRACT.md);
  roster: [`agents/_shared/RUNTIME_VS_BUILD.md`](./agents/_shared/RUNTIME_VS_BUILD.md);
  skill map: [`agents/_shared/SKILL_MAP.md`](./agents/_shared/SKILL_MAP.md).
- **MCP server** (`mcp/sfskills-mcp/`) — 38 tools across skill / agent /
  template / decision-tree retrieval plus live-org metadata and read-only
  SOQL, so the agent can answer "does this already exist in my org?" without
  asking you.

Shipped in v1:

- [x] 1027 skills across Admin, Apex, LWC, Flow, OmniStudio, Agentforce, Security, Integration, Data, Architect, DevOps
- [x] Shared Apex / LWC / Flow / Agentforce templates and four decision trees
- [x] Golden evals for 10 flagship skills (3 P0 cases each)
- [x] MCP server on PyPI exposing the library plus live-org lookups

Queue for what comes next: [`BACKLOG.yaml`](./BACKLOG.yaml) ·
[`docs/queue-progress.md`](./docs/queue-progress.md).

---

## MCP server

38 read-only tools — the fifteen listed here cover the usual paths:
`search_skill` (lexical search
over the 1027-skill SfSkills corpus), `get_skill`, `get_agent`, `list_agents`,
`describe_org`, `list_custom_objects`, `list_flows_on_object`,
`list_validation_rules`, `list_permission_sets`, `describe_permission_set`,
`list_record_types`, `list_named_credentials`, `list_approval_processes`,
`validate_against_org`, and `tooling_query`. Every tool carries honest MCP
annotations so clients can auto-approve safely; no secrets enter the process.

Setup for Claude Code, Claude Desktop, Cursor, Windsurf, Zed, VS Code, Cline,
Continue, Codex CLI, Gemini CLI, Goose and the generic stdio transport:
[`mcp/sfskills-mcp/docs/CONNECT.md`](./mcp/sfskills-mcp/docs/CONNECT.md).
Tool schemas and design notes: [`mcp/sfskills-mcp/README.md`](./mcp/sfskills-mcp/README.md).

---

## More

- [`docs/installing.md`](./docs/installing.md) — canonical setup reference: the one bootstrap command, every flag, what a fresh clone does and does not contain, embeddings cost, MCP install paths
- [`docs/installing-the-plugin.md`](./docs/installing-the-plugin.md) — install the library as a Claude Code plugin
- [`docs/README.md`](./docs/README.md) — documentation hub: getting started, architecture, FAQ, troubleshooting
- [`docs/positioning.md`](./docs/positioning.md) — what this project claims, and what it refuses to claim
- [`docs/comparison.md`](./docs/comparison.md) — how it compares to the alternatives, including where it loses
- [`docs/go-to-market.md`](./docs/go-to-market.md) — the launch plan
- [`docs/installing-single-agents.md`](./docs/installing-single-agents.md) — ship one agent into another project
- [`CONTRIBUTING.md`](./CONTRIBUTING.md) — add a skill, fix a skill, report a gap, flag stale content

---

**Pranav Nagrecha** — Salesforce Technical Architect ·
[Issues](https://github.com/PranavNagrecha/AwesomeSalesforceSkills/issues) ·
Apache-2.0 ([LICENSE](./LICENSE))

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