Agent skill

Dx Apexguru Scan

by forcedotcom in forcedotcom/sf-skills

Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API.

Apache-2.0Auto-check passedSales & Support

Install Dx Apexguru Scan

skills CLI
$ npx skills add forcedotcom/sf-skills --skill dx-apexguru-scan -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install forcedotcom/sf-skills dx-apexguru-scan --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/forcedotcom/sf-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dx-apexguru-scan .claude/skills/dx-apexguru-scan && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dx-apexguru-scan
GitHub stars
1.1k
Token cost
~5.1k tokens
SKILL.md length
2,165 words
Files
15 (incl. scripts, references)
Skills in repo
252
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API.

  • Works in 5 steps: Identify the project root → Package the project → Submit and poll → …
  • The user says run ApexGuru
  • SKILL.md covers CRITICAL: Mandatory Script Usage, CRITICAL: Present --present…, Overview and Prerequisites, plus 3 more sections
  • Runs Shell and JavaScript scripts from its folder; calls bash, node and jq; reaches api.salesforce.com; needs APEXGURU_SFAP_TOKEN

What it does

Dx Apexguru Scan is an agent skill from forcedotcom/sf-skills. Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API. Zips the project's Apex (any layout), submits it, polls to completion, decodes the base64 report, and presents performance antipattern violations (SOQL in loop, DML in loop, Schema.getGlobalDescribe(), SOQL without WHERE/LIMIT, unused SOQL fields) grouped by rule with severity, file:line, and suggested fixes — clearly attributed as 'Static only' or 'Production insights'. TRIGGER when the user says 'run ApexGuru'…

Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts and reference files (for example `examples/README.md`, `examples/sample-decoded-summary.json` and `examples/sample-full-no-runtime-response.json`).

It sits in Sales & Support, covering Static analysis and SAST, CRM management and Security review. It works with Salesforce. The repository describes itself as: Salesforce's curated collection of agent skills for building applications. Optimized for Agentforce Vibes, compatible with all AI tools. The licence is Apache-2.0.

When your agent uses it

  • The user says run ApexGuru
  • Check Apex performance
  • Find governor-limit / performance antipatterns
  • Scan my Apex for performance

Example prompts

  • “Static only”
  • “Production insights”
  • “run ApexGuru”
  • “/dx-apexguru-scan”

Requirements

  • Python 3
  • Node.js
  • A Bash shell
  • A credential in APEXGURU_SFAP_TOKEN
  • Pre-approved tools (allowed-tools): Read, Bash(bash), Bash(node), Bash(curl), Bash(zip), Bash(unzip), Bash(jq), Bash(sf), Bash(date), Write

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify the project root
  2. Package the project
  3. Submit and poll
  4. Decode and present
  5. Drill into results (no re-scan)

What it can do on your machine

Read from SKILL.md and the folder at commit 4bbae5c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash(bash)
    • Bash(node)
    • Bash(curl)
    • Bash(zip)
    • Bash(unzip)
    • Bash(jq)
    • Bash(sf)
    • Bash(date)
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 6 files in scripts/ (Shell and JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • bash
    • node
    • jq
    • curl
    • sf

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.salesforce.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • APEXGURU_SFAP_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dx Apexguru Scan loads about 5.1k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 216 tokens; SKILL.md has 2,165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~216
When it runs · the whole SKILL.md, loaded when a task matches
~5.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from forcedotcom/sf-skills at commit 4bbae5c, republished under its Apache-2.0 licence (© forcedotcom). 2,165 words, ~5,101 tokens.

Download SKILL.mdSave it as .claude/skills/dx-apexguru-scan/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
dx-apexguru-scan
description
Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API. Zips the project's Apex (any layout), submits it, polls to completion, decodes the base64 report, and presents performance antipattern violations (SOQL in loop, DML in loop, Schema.getGlobalDescribe(), SOQL without WHERE/LIMIT, unused SOQL fields) grouped by rule with severity, file:line, and suggested fixes — clearly attributed as 'Static only' or 'Production insights'. TRIGGER when the user says 'run ApexGuru', 'ApexGuru scan', 'check Apex performance', 'find governor-limit / performance antipatterns', 'SOQL in loop', 'scan my Apex for performance', or 'ApexGuru performance insights'. DO NOT TRIGGER for general static analysis or security scans (use dx-code-analyzer-run), for fixing code without scanning, or for onboarding an org to ApexGuru.
allowed-tools
Read, Bash(bash), Bash(node), Bash(curl), Bash(zip), Bash(unzip), Bash(jq), Bash(sf), Bash(date), Write
argument-hint
[project-path] [--org <alias>] [--fast]
metadata.version
1.1
metadata.domains
Developer Experience
metadata.relatedSkills
dx-code-analyzer-run

ApexGuru Performance Scan Skill

CRITICAL: Mandatory Script Usage

Every step — token resolution, zipping, API calls, and report decoding — MUST go through the bundled scripts in <skill_dir>/scripts/. No exceptions.

WRONG — never do this:
bash
# WRONG: hand-rolled curl to the API
curl -X POST https://api.salesforce.com/... -F file=@x.zip

# WRONG: inline base64 + jq to read the report
cat raw.json | jq -r .report | base64 -d | jq '.[]'

# WRONG: reading the raw result file directly (report is a large base64 blob)
Read tool → apexguru-raw-*.json

# WRONG: inline node/python to parse violations
node -e "const r = require('./raw.json'); ..."
RIGHT — always do this:
bash
# PREFERRED — one command runs all three steps (package → submit+poll →
# decode+present) and prints the ready-to-show report as its final stdout.
# Use this for every initial scan: it cannot be left half-finished.
bash "<skill_dir>/scripts/scan.sh" "<project-root>"

# Optionally persist the presented markdown to a file as well:
bash "<skill_dir>/scripts/scan.sh" "<project-root>" --out ./apexguru-report.md

The three underlying scripts still exist and scan.sh calls them in order. Invoke them individually only for drill-downs on an already-scanned result (Step 5), or when you deliberately need to inspect an intermediate artifact:

bash
# Equivalent manual chain (scan.sh runs exactly these, in this order):
bash "<skill_dir>/scripts/build-zip.sh" "<project-root>" "./apexguru-<TS>.zip"
bash "<skill_dir>/scripts/run-scan.sh"  "./apexguru-<TS>.zip" "./apexguru-raw-<TS>.json"
node "<skill_dir>/scripts/decode-report.js" "./apexguru-raw-<TS>.json" --present

# Drill into a subset WITHOUT re-scanning (reuse the raw file scan.sh left, or
# pass --raw to scan.sh to keep it at a known path):
node "<skill_dir>/scripts/decode-report.js" "./apexguru-raw-<TS>.json" --rule SOQL_IN_LOOP --full
node "<skill_dir>/scripts/decode-report.js" "./apexguru-raw-<TS>.json" --group file --top 5

<skill_dir> is the absolute path to the directory containing this SKILL.md. Never use ./scripts/ — that resolves against the user's CWD, not the skill dir.

Any filter/rank/group question ("which file has the most issues?", "show only SOQL-in-loop", "break down by severity") is answered by re-running decode-report.js with flags against the same raw result file — never re-scan, never parse the JSON by hand.


CRITICAL: Present --present output verbatim — never condense it

decode-report.js --present (Step 4) already produces the final, ready-to-show markdown: severity legend, one detail card per violation (message, code, fix, resource link), and a closing summary table. That stdout is the response. Print it to the user exactly as printed — do not rewrite it into a shorter table, do not drop the per-issue cards down to just the summary table, and do not wait for the user to ask "explain a violation" before including message/fix/resource. Condensing it defeats the entire point of --present.

The attribution is already in that stdout — the summary line is the exact output that states the mode (e.g. "ApexGuru (static analysis) is active. To unlock runtime intelligence…"). Do NOT prepend or append your own attribution sentence (no "Attribution: analysisMode: static…", no naming the org, no restating "static-only findings"). The script's line is the complete, approved wording; adding your own makes the output non-deterministic and off-message.

WRONG — never do this:
text
Top Issues (worst first)
#  Severity   Rule                      Method    Line
1  Major      UsingTheTestMethodKeyword legacy... 136
...
Key Antipatterns Detected:
- SOQL/DML in loops (3 violations)

(a hand-built summary that drops every message/code/fix — even for violations that had one)

text
Attribution: analysisMode: static — source-only analysis. The scanned org
(ag-skills-org) is not onboarded to ApexGuru's full runtime metrics, so
these are static-only findings.

(an agent-authored attribution line prepended to the report — the script's own summary line already states the mode; this duplicate is non-deterministic and names an org the script never had access to)

RIGHT — always do this:

Paste the full stdout from decode-report.js --present — every ### Issue N card and the closing ## Summary table — unedited, in one response.


Overview

ApexGuru detects performance antipatterns in Apex (SOQL/DML in loops, Schema.getGlobalDescribe(), SOQL without WHERE/LIMIT, unused SOQL fields). This skill drives the ApexGuru SFAP Scan API: it packages the user's Apex (every .cls/.trigger under the project root, any layout) into a zip, submits it, polls until the scan finishes, decodes the base64-encoded report, and presents violations grouped by rule with severity, file:line, and suggested fixes.

Attribution is mandatory. The API returns analysisMode:

  • static → source-only analysis → label results "Static only".
  • full → enriched with runtime metrics from an org onboarded to ApexGuru → label results "Production insights".

decode-report.js --present already renders this attribution into its summary line and title ("Static only" / "Production insights") — that satisfies the mandatory-attribution requirement. Print that line as the exact output; do not author your own attribution sentence or name the org. If the user expected full but got static, the script's static-mode line already explains the org is not onboarded — point them to it rather than restating it (see error handling).

In scope: zipping a project's Apex, submitting/polling the scan, decoding + presenting violations, filtering/grouping existing results, troubleshooting API errors.

Out of scope: general static analysis / security / lint (→ dx-code-analyzer-run, which lists ApexGuru as an engine), applying fixes to code, onboarding an org to ApexGuru, minting SFAP tokens.


Prerequisites

  • An authenticated sf CLI org (sf org login web ...). resolve-token.sh derives the SFAP JWT from it via <instanceUrl>/ide/auth — this is the normal IDE-session path. Alternatively, set APEXGURU_SFAP_TOKEN / APEXGURU_SFAP_TOKEN_FILE to supply a JWT directly (CI/headless). The org is derived from the token's tnk claim — no org id is passed. Pass --org <alias> to pick a specific org. See <skill_dir>/references/authentication.md. If no token can be resolved, the script returns a clear error with a hint.
  • sf, bash, curl, zip, jq, node on PATH (standard on macOS/Linux dev boxes).
  • A folder containing Apex — an sfdx project, a force-app/ subtree, or any folder with .cls/.trigger files. build-zip.sh collects all Apex beneath it regardless of layout; the API walks the whole archive.

Workflow

Step 1: Identify the project root

The project root is any folder that contains Apex somewhere beneath it (usually an sfdx project root next to sfdx-project.json, but a force-app/ subtree or a loose folder of .cls files works too). If the user gave a path, use it; otherwise use the current working directory. build-zip.sh collects every .cls/.trigger under it (any layout) and fails clearly if none exists.

Step 2: Package the project
bash
TS=$(date +%Y%m%d-%H%M%S)
bash "<skill_dir>/scripts/build-zip.sh" "<project-root>" "./apexguru-${TS}.zip"

Output JSON gives zip, bytes, humanSize, apexFileCount, scanRoot. The script enforces the 200MB compressed limit and fails fast if exceeded. On error (error/hint fields), relay the hint and stop.

Step 3: Submit and poll
bash
bash "<skill_dir>/scripts/run-scan.sh" "./apexguru-${TS}.zip" "./apexguru-raw-${TS}.json"
  • Add --fast if the user wants a quicker/cheaper run (skips LLM-heavy fix generation).
  • The endpoint follows the token's environment — the base URL is derived from the token's tnk claim: a prod org hits api.salesforce.com, and an internal stage/dev org hits stage./dev.api.salesforce.com. Customers authenticate a prod org, so they always hit prod; no extra flags or config.
  • --org <alias> picks which authenticated sf org the JWT is derived from (omit to use the CLI's default org).
  • Progress (QUEUED → RUNNING → SUCCEEDED) streams to stderr; the script polls ~every 15s. Default ceiling is 10 min (--max-polls, --interval to adjust).
  • On success, stdout is a one-line JSON summary and the full raw body is written to apexguru-raw-${TS}.json. On failure, stdout is {error, httpStatus, status, hint} — relay the hint. For status-code specifics see <skill_dir>/references/error-handling.md.
  • Foreground only. Do not background this; polling output must be observed.
  • A SUCCEEDED scan is not the finish line. The raw result is a base64 blob, not a user-facing answer. Do not stop or report "done" after the scan succeeds — you MUST continue to Step 4 to decode and present the report. Ending the turn at Step 3 leaves the user with nothing readable.
Step 4: Decode and present
bash
node "<skill_dir>/scripts/decode-report.js" "./apexguru-raw-${TS}.json" --present

--present is the default way to decode for presentation: it implies --full (no silent caps) and prints ready-to-show markdown directly — a severity legend (Minor / Major / Critical, plus a Tip marker when analysisMode: full enriches severity from production metrics), one ### Issue N card per violation (message, current code, suggested fix, help-doc link) for the non-hotspot rules — capped at --top (default 10) worst-first, with the cap stated in the heading — and a closing ## Summary table listing every violation regardless of the card cap. ExpensiveMethods (a per-method CPU-hotspot ranking from full mode, not a line-level antipattern) is collapsed into its own ranked "CPU Hotspots" table instead of repeating a near-identical card per method. Print this output to the user verbatim — present immediately — do not pause to ask, and do not re-summarize it into a shorter table.

For Step 5 drill-downs (filtering/grouping an existing result), the bare (non---present) JSON form is fine — see the reading rules below, which apply whenever you run the script without --present.

DO NOT: invent script code, use bare ./scripts/... paths, decode base64 inline, jq the report field, or Read the raw file directly.

Show full SKILL.md (991 more words)Show less
Instructions for reading bare (non---present) decode-report.js output

The command prints one JSON object to stdout. Read it field by field before presenting anything — do not eyeball a partial view as complete:

  1. Check truncated first, before anything else. If true, groups was capped to the top --top (default 10) rules, each group's sample was capped to 3 items, and topViolations was capped to --top items. Never present a truncated:true result as the full picture. Re-run the same command with --full appended and use that output instead. Only skip this if the user explicitly asked for a quick/partial look.
  2. State attribution from analysisMode/attribution — static/"Static only" or full/"Production insights". This is mandatory on every response, per "Attribution is mandatory" above.
  3. serverViolationBreakdown is the raw API's internal rule-code tally (e.g. SOQL_IN_LOOP_1HOP, GGD) — it's a sanity-check total (sums to violationCount), not a display name. Never show these codes to the user; use the human-readable groups[].key names instead (e.g. SoqlInALoopOneHop, SchemaGetGlobalDescribeNotEfficient).
  4. severityCounts (top-level) is the severity distribution across ALL violations — use it for the summary table. Each groups[] entry has its own severityCounts scoped to just that rule.
  5. Build the "Violations by Rule" table from groups, one row per entry: key → Rule, count → Count, severityCounts → Severity, and one sample[0] (or items[0] when --full) → Example (file:line).
  6. Build the "Top Issues" table from topViolations — already sorted worst-severity-first. Use rule, severity, file:line, and the first entry of fixes (if non-empty) as Suggested Fix. If fixes is empty, omit that column's value rather than inventing a fix.
  7. When the user asks to explain a specific violation ("what does this mean", "why is this flagged"), surface that violation's message (plain- language why) and resources[0] (Help Doc URL) verbatim — both exist on every violation object but are intentionally left out of the summary tables in step 5/6 to keep those scannable. Fall back to references/violation-catalog.md only if message is empty.
  8. fixes being [] is expected, not an error — the API's suggestions field (fix code) isn't populated for every rule (notably ExpensiveMethods, a CPU ranking with no single-line fix); don't say "no fix available", just omit the column.
  9. With --full, each group also carries an items array (every violation for that rule, not just the 3-item sample) — use items instead of sample when the user wants the complete list for one rule ("show me all the SOQL unused-fields ones").
Presentation template (fallback — only when NOT using --present)

--present (the default, per Step 4 above) already renders the full severity-legend + issue-cards + summary-table output described in the "Instructions for reading bare output" section — just print its stdout verbatim. Only build a table by hand from bare JSON if --present genuinely can't be used (e.g. scripting/CI context with no markdown renderer):

Filling the <Static only | Production insights> title placeholder: derive the label from the attribution field (not analysisMode alone) — it already encodes the three states:

  • "Production insights" (analysisMode: full with runtime metrics) — enriched with production runtime metrics.
  • "Static only" + analysisMode: full (no runtime metrics) — org is onboarded, but there's no runtime data for this code yet; generate a runtime report in Scale Center.
  • "Static only" + analysisMode: static — source-only. Onboard the org to ApexGuru for production insights.

The fenced block below is the literal rendered output — substitute the real values and print it; do not emit any of the guidance above:

text
## ApexGuru Scan Complete — <Static only | Production insights>

**Found X performance violations** across Y files.

| Severity | Count |
|----------|-------|
| Critical (1) | X |
| High (2) | X |
| Moderate (3) | X |

### Violations by Rule
| Rule | Count | Severity | Example |
|------|-------|----------|---------|
| SOQL_IN_LOOP | 15 | High (2) | AccountService.cls:42 |
| DML_IN_LOOP | 8 | Critical (1) | AccountService.cls:60 |
| GGD | 2 | Moderate (3) | Utils.cls:12 |

### Top Issues
| # | Rule | Sev | File:Line | Suggested Fix |
|---|------|-----|-----------|---------------|
| 1 | DML_IN_LOOP | 1 | AccountService.cls:60 | Collect records; DML once after the loop |
| ... up to 10 |

Raw result: `./apexguru-raw-<TS>.json`

Scale to result size: 0 → "no performance antipatterns found"; 1–10 → one table; 11+ → severity counts + by-rule table + top 10. End with the raw result path. Do not append your own follow-up offer (no "I can drill in without re-scanning…", no "filter by rule / group by file / explain a violation" menu) — --present already prints the script's "show all" footer; that is the complete, approved closing line and adding your own makes the output non-deterministic. Rule-catalog details: <skill_dir>/references/violation-catalog.md.

Step 5: Drill into results (no re-scan)

Re-run decode-report.js against the same raw file with flags:

User saysFlags
"show only SOQL-in-loop"--rule SOQL_IN_LOOP --full
"just the critical ones"--severity 1
"what's in AccountService.cls?"--file AccountService.cls --full
"group by file" / "which file is worst?"--group file --top 5
"break down by severity"--group severity
"show me everything"--present (or --full for bare JSON)

Constraints & Gotchas

ItemWhy / Fix
Run scripts with absolute <skill_dir> path./scripts/ resolves against the user's CWD, not the skill dir
Any project layout is fineThe API walks the whole archive for Apex; build-zip.sh collects every .cls/.trigger under the root, no force-app/ required
Never decode report inlineIt is a large base64 blob — always use decode-report.js
Use --present for the initial decodeImplies --full (no silent caps) and renders ready-to-show markdown directly — severity legend, per-issue cards, closing summary table — mirroring the reference MCP tool's presentation density
Never re-scan to filterStep 5 re-decodes the existing raw file instantly
Attribution is pre-rendered--present already prints the mode line ("Static only" / "Production insights") — print it as the exact output; never author your own attribution sentence or name the org
static when full expectedOrg not onboarded to ApexGuru — tell the user, don't treat as an error
401 / 403 / 404 / 400Token / org-ownership / scanId / zip issues — see references/error-handling.md
Foreground only, ~15s pollsBackgrounding loses progress; scans can take minutes
Token is a secretresolve-token.sh never echoes it; don't print it or write it to result files
Not a security/lint scannerFor PMD/ESLint/security, use dx-code-analyzer-run

Reference & Script Index

Scripts (execute via bash/node with the absolute <skill_dir>/ prefix, never Read):

FileWhen to use
<skill_dir>/scripts/resolve-token.shResolve SFAP JWT + base URL (called by run-scan.sh)
<skill_dir>/scripts/validate-token.jsLocal (no-network) JWT pre-flight: env/scope/expiry (called by resolve-token.sh)
<skill_dir>/scripts/build-zip.shStep 2 — collect the project's Apex into a size-checked zip
<skill_dir>/scripts/run-scan.shStep 3 — submit + poll to completion
<skill_dir>/scripts/decode-report.jsSteps 4–5 — decode base64 report, group/filter violations

References (read on demand):

FileWhen to read
references/authentication.mdWhere the SFAP JWT comes from; env-var/file setup
references/api-reference.mdEndpoint contracts, request/response shapes, limits
references/violation-catalog.mdApexGuru rule meanings and typical fixes
references/error-handling.md400/401/403/404, FAILED, timeout, static-vs-full diagnosis

examples/ contains a sample SUCCEEDED response and a decoded-summary sample.

© forcedotcom, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 14 other files (scripts, references) in skills/dx-apexguru-scan of forcedotcom/sf-skills.

  • SKILL.md
  • examples/README.md
  • examples/sample-decoded-summary.json
  • examples/sample-full-no-runtime-response.json
  • examples/sample-succeeded-response.json
  • references/api-reference.md
  • references/authentication.md
  • references/error-handling.md
  • references/violation-catalog.md
  • scripts/build-zip.sh
  • scripts/decode-report.js
  • scripts/resolve-token.sh
  • scripts/run-scan.sh
  • scripts/scan.sh
  • scripts/validate-token.js

Open the folder on GitHubat commit 4bbae5c

Compare with similar skills

Dx Apexguru Scan next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dx Apexguru Scan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dx Apexguru Scan this skillforcedotcom/sf-skills1.1k—~5.1kAutomated safety check: PassApache-2.0
Soql Lib Query Builderbeyond-the-cloud-dev/soql-lib154—~4.3kAutomated safety check: PassMIT
Sf DatacloudJaganpro/sf-skills424—~2.7kAutomated safety check: PassMIT
Soql Lib Selectorbeyond-the-cloud-dev/soql-lib154—~2kAutomated safety check: PassMIT
Dev SetupPortwood-Global-Solutions/Portwood126—~1.1kAutomated safety check: PassApache-2.0
Sf FlowJaganpro/sf-skills424—~1.8kAutomated safety check: PassMIT

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Works with

Questions about Dx Apexguru Scan

What does Dx Apexguru Scan do?

Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API. Dx Apexguru Scan is an agent skill from forcedotcom/sf-skills. Run an ApexGuru performance scan on a Salesforce Apex project via the ApexGuru SFAP Scan API.

When should I use Dx Apexguru Scan?

Dx Apexguru Scan fits situations like: the user says run ApexGuru; check Apex performance; find governor-limit / performance antipatterns; scan my Apex for performance.

How do I install Dx Apexguru Scan in Claude Code?

Run `npx skills add forcedotcom/sf-skills --skill dx-apexguru-scan -a claude-code`. Or copy the skill folder (skills/dx-apexguru-scan in forcedotcom/sf-skills) into .claude/skills/dx-apexguru-scan in your project. Claude Code loads it when a task matches its description.

How do I install Dx Apexguru Scan in Codex?

Run `npx skills add forcedotcom/sf-skills --skill dx-apexguru-scan -a codex`. Or copy the skill folder (skills/dx-apexguru-scan in forcedotcom/sf-skills) into .agents/skills/dx-apexguru-scan in your project. Codex loads it when a task matches its description.

Can I use Dx Apexguru Scan in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add forcedotcom/sf-skills --skill dx-apexguru-scan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dx-apexguru-scan, .gemini/skills/dx-apexguru-scan, .github/skills/dx-apexguru-scan and .opencode/skills/dx-apexguru-scan in your project.

What does Dx Apexguru Scan need to run?

Going by SKILL.md and its folder, Dx Apexguru Scan needs a shell and JavaScript for the scripts in its folder, the command-line tools its instructions call (bash, node, jq, curl and sf) and credentials named APEXGURU_SFAP_TOKEN. Our summary lists: Python 3; Node.js; A Bash shell; A credential in APEXGURU_SFAP_TOKEN. Its frontmatter pre-approves these tools: Read, Bash(bash), Bash(node), Bash(curl), Bash(zip), Bash(unzip), Bash(jq), Bash(sf), Bash(date), Write.

Does Dx Apexguru Scan access the network?

SKILL.md names 1 domain. In commands or code: api.salesforce.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Dx Apexguru Scan safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Dx Apexguru Scan use?

Dx Apexguru Scan is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dx Apexguru Scan use?

About 5.1k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Dx Apexguru Scan?

Skills that share tags, products or a category with Dx Apexguru Scan: Soql Lib Query Builder (beyond-the-cloud-dev/soql-lib, 154 stars), Sf Datacloud (Jaganpro/sf-skills, 424 stars), Soql Lib Selector (beyond-the-cloud-dev/soql-lib, 154 stars) and Dev Setup (Portwood-Global-Solutions/Portwood, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dx Apexguru Scan?

forcedotcom (a GitHub organization) maintains it in forcedotcom/sf-skills, which has 1,067 GitHub stars. The repository holds 252 skills in this directory. The repository was last updated on October 9, 2026.

Source: forcedotcom/sf-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.