New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Sf Flex Estimator

by Jaganpro in Jaganpro/sf-skills

Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads.

MITAuto-check passedSales & Support

Install Sf Flex Estimator

skills CLI
$ npx skills add Jaganpro/sf-skills --skill sf-flex-estimator -a claude-code

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

GitHub CLI
$ gh skill install Jaganpro/sf-skills sf-flex-estimator --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/Jaganpro/sf-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sf-flex-estimator .claude/skills/sf-flex-estimator && 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
sf-flex-estimator
GitHub stars
424
Token cost
~2.1k tokens
SKILL.md length
707 words
Files
16 (incl. references, assets)
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads.

  • Works in 5 steps: Baseline the structure → Calculate the per-invocation cost → Calculate Data Cloud base FC → …
  • : user needs cost projections
  • SKILL.md covers When This Skill Owns the Task, Required Context to Gather First, Core Pricing Model and Recommended Workflow, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Sf Flex Estimator is an agent skill from Jaganpro/sf-skills. Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads. TRIGGER when: user needs cost projections, scenario planning, budget sizing, or architecture tradeoff analysis for Agentforce prompts/actions, Data Cloud meters, or monthly Flex Credit usage. DO NOT TRIGGER when: user is building Agentforce metadata or .agent files themselves (use sf-ai-agentforce or sf-ai-agentscript), implementing Data Cloud assets (use sf-datacloud-), or asking for contract-specific commercial approval that depends on…

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including reference files and assets (for example `CREDITS.md`, `README.md` and `assets/calculators/flex_calculator.py`).

It sits in Sales & Support, covering CRM management. It works with Salesforce. The repository describes itself as: [ARCHIVED — migrated to forcedotcom/afv-library] Salesforce Skills for Agentic Coding Tools — Apex, Flow, LWC, SOQL, Agentforce, Data Cloud, OmniStudio. Read-only archive; active… The licence is MIT.

When your agent uses it

  • : user needs cost projections
  • Scenario planning
  • Architecture tradeoff analysis for Agentforce prompts/actions
  • Data Cloud meters

Example prompts

  • “/sf-flex-estimator”

Requirements

  • Python 3

Workflow steps

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

  1. Baseline the structure
  2. Calculate the per-invocation cost
  3. Calculate Data Cloud base FC
  4. Generate scenarios
  5. Validate assumptions and recommend optimizations

What it can do on your machine

Read from SKILL.md and the folder at commit 53c9956. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

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

  • Network

    No URLs in SKILL.md.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Sf Flex Estimator loads about 2.1k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 140 tokens; SKILL.md has 707 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~140
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from Jaganpro/sf-skills at commit 53c9956, republished under its MIT licence (© Jaganpro). 707 words, ~2,124 tokens.

Download SKILL.mdSave it as .claude/skills/sf-flex-estimator/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.
name
sf-flex-estimator
description
Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads. TRIGGER when: user needs cost projections, scenario planning, budget sizing, or architecture tradeoff analysis for Agentforce prompts/actions, Data Cloud meters, or monthly Flex Credit usage. DO NOT TRIGGER when: user is building Agentforce metadata or .agent files themselves (use sf-ai-agentforce or sf-ai-agentscript), implementing Data Cloud assets (use sf-datacloud-*), or asking for contract-specific commercial approval that depends on non-public pricing terms.
license
MIT
metadata.version
1.1.0
metadata.author
August Krys
metadata.scoring
130 points across 7 categories

sf-flex-estimator: Agentforce & Data Cloud Flex Credit Estimation

Use this skill when the user needs a public-price estimate for:

  • Agentforce prompt + action consumption
  • Data Cloud monthly usage meters
  • Flex Credit scenario planning
  • cost optimization recommendations before build or rollout

This skill is for planning and estimation, not implementation.

When This Skill Owns the Task

Use sf-flex-estimator when the user is asking questions like:

  • "What will this Agentforce agent cost per month?"
  • "Estimate Flex Credits for 5 prompts, 8 actions, and Data Cloud grounding"
  • "Compare low / medium / high usage scenarios"
  • "How much does Private Connect add?"
  • "What Flex Credit savings do we get if we reduce streaming or action count?"

Delegate elsewhere when the user is:

  • building Builder metadata, Prompt Builder templates, or action wiring → sf-ai-agentforce
  • authoring or fixing .agent files → sf-ai-agentscript
  • implementing Data Cloud connections, streams, DMOs, segments, or activations → sf-datacloud and the phase-specific sf-datacloud-* skills
  • creating test data or operational data imports → sf-data
  • deploying metadata or runtime assets → sf-deploy

Required Context to Gather First

Ask for or infer:

  • agent prompt count by tier: starter, basic, standard, advanced
  • action count by type: standard, custom, voice, sandbox
  • whether token overages are expected for prompts or actions
  • monthly Data Cloud meter volumes, if Data Cloud is in scope
  • whether Private Connect is required
  • whether the estimate should model a pilot, small production, enterprise, or multiple scenarios
  • whether the user wants public list-price guidance or is trying to reconcile contract-specific commercial numbers

If the user does not know exact monthly volumes, start with a baseline template and generate multiple scenarios.

Core Pricing Model

Agentforce

Agentforce billing is linear — no volume tiers.

ComponentFC per invocation
Starter prompt2
Basic prompt2
Standard prompt4
Advanced prompt16
Standard / custom action20
Voice action30
Sandbox action16
Data Cloud

Data Cloud uses monthly cumulative tiering.

TierMonthly FC rangeMultiplier
Tier 10 - 300K1.0x
Tier 2300K - 1.5M0.8x
Tier 31.5M - 12.5M0.4x
Tier 412.5M+0.2x
Other rules
  • Flex Credits are priced at $0.004 per FC in this skill.
  • Private Connect adds 20% of Data Cloud spend after tiering.
  • Agentforce and Data Cloud are estimated separately, then combined.
  • Estimates in this skill use publicly documented list pricing only.

For the full meter table and examples, read:

1. Baseline the structure

Model the agent and Data Cloud footprint first.

Useful starting templates:

2. Calculate the per-invocation cost

For Agentforce, estimate:

text
per-invocation FC = prompt FC + action FC + token overage FC
3. Calculate Data Cloud base FC

Map each monthly meter volume to the current public rate card, then apply cumulative tiering.

Show full SKILL.md (281 more words)Show less
4. Generate scenarios

Use the standard scenario set unless the user provides a better one:

  • Low: 1K invocations / month
  • Medium: 10K / month
  • High: 100K / month
  • Enterprise: 500K / month
5. Validate assumptions and recommend optimizations

Check for:

  • too many prompts or actions
  • unnecessary streaming usage
  • likely token overages
  • missing Private Connect handling
  • unrealistic volume assumptions

Scripts and Templates

Calculator
Validation helper

This validator is a manual helper. It is intentionally not wired into the shared auto-validation dispatcher because generic .json or .md file patterns would create too much noise.

Example commands
bash
# Per-invocation estimate for a template
python3 assets/calculators/flex_calculator.py \
  --mode structure \
  --agent-def assets/templates/basic-agent-template.json

# Scenario estimate for an Agentforce + Data Cloud design
python3 assets/calculators/flex_calculator.py \
  --mode scenarios \
  --agent-def assets/templates/hybrid-agent-template.json

# Tiering only
python3 assets/calculators/tier_multiplier.py \
  --base-fc 5000000 \
  --pretty

# Validate an estimate input document
python3 hooks/scripts/validate_estimate.py \
  --input assets/templates/hybrid-agent-template.json \
  --verbose

High-Signal Estimation Rules

  • Prefer standard prompts for most production reasoning workloads.
  • Use basic prompts only for simple routing/classification.
  • Action count often dominates cost faster than prompt count.
  • Data Cloud streaming is materially more expensive than prep/query/segment meters.
  • Tiering matters only for Data Cloud, not Agentforce.
  • Private Connect applies only to Data Cloud spend in this model.
  • If the user has contract-specific pricing, treat this skill as a public baseline and note that commercial terms may differ.

Output Format

When the estimate is complete, present:

  1. workload summary
  2. per-invocation Agentforce cost
  3. monthly scenario table
  4. Data Cloud tiering impact
  5. top optimization recommendations
  6. confidence / validation notes

Suggested shape:

text
Flex Credit estimate: <name>
Agentforce per invocation: <fc> FC ($<cost>)
Data Cloud monthly base: <fc> FC
Scenarios: <low / medium / high / enterprise>
Optimization priorities: <1-3 bullets>
Confidence: <high / medium / low>

Cross-Skill Integration

NeedDelegate toWhy
build the actual agent metadatasf-ai-agentforceimplementation of Builder assets
build a deterministic .agent bundlesf-ai-agentscriptauthoring and validation of Agent Script
implement Data Cloud pipeline assetssf-datacloud and sf-datacloud-*live Data Cloud setup
package or deploy the solutionsf-deploydeployment workflow
generate supporting test or sample datasf-datadata preparation

A common chain is:

text
sf-ai-agentforce / sf-ai-agentscript / sf-datacloud-* → sf-flex-estimator → sf-deploy

Reference Map

Start here
Pricing references
Validation and scoring

© Jaganpro, MIT. 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 15 other files (references, assets) in skills/sf-flex-estimator of Jaganpro/sf-skills.

  • SKILL.md
  • CREDITS.md
  • LICENSE
  • README.md
  • assets/calculators/flex_calculator.py
  • assets/calculators/tier_multiplier.py
  • assets/templates/basic-agent-template.json
  • assets/templates/data-cloud-template.json
  • assets/templates/hybrid-agent-template.json
  • hooks/scripts/validate_estimate.py
  • references/agentforce-pricing.md
  • references/calculation-methodology.md
  • references/common-use-cases.md
  • references/data-cloud-pricing.md
  • references/edge-cases.md
  • … and 1 more

Open the folder on GitHubat commit 53c9956

Compare with similar skills

Sf Flex Estimator 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.

Sf Flex Estimator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sf Flex Estimator this skillJaganpro/sf-skills424—~2.1kAutomated safety check: PassMIT
Soql Lib Query Builderbeyond-the-cloud-dev/soql-lib154—~4.3kAutomated 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
Automation Sandbox Post Copy Configureforcedotcom/sf-skills1.1k—~5.3kAutomated safety check: NotesApache-2.0
Automation Sandbox Post Copy Configureforcedotcom/sf-skills1.1k—~5.4kAutomated safety check: NotesApache-2.0

Similar skills

  • Soql Lib Query Builder

    beyond-the-cloud-dev/soql-lib

    Builds Salesforce SOQL queries using the SOQL Lib fluent builder API (SOQL.cls).

    154 GitHub stars~4.3k tokensUpdated 6 days ago
    Sales & SupportAuto-check passed

  • Soql Lib Selector

    beyond-the-cloud-dev/soql-lib

    Creates Salesforce Apex selector classes using the SOQL Lib selector pattern.

    154 GitHub stars~2k tokensUpdated 6 days ago
    Sales & SupportAuto-check passed

  • Dev Setup

    Portwood-Global-Solutions/Portwood

    Get from a fresh clone of Portwood to a working, fully-tested Salesforce org.

    126 GitHub stars~1.1k tokensUpdated today
    Sales & SupportAuto-check passed

  • Automation Sandbox Post Copy Configure

    forcedotcom/sf-skills

    Apply a Salesforce sandbox post-copy automation JSON config against a target org.

    1.1k GitHub stars~5.3k tokensUpdated 2 days ago
    Sales & SupportAuto-check: notes

  • Automation Sandbox Post Copy Configure

    forcedotcom/sf-skills

    Apply a Salesforce sandbox post-copy automation JSON config against a target org.

    1.1k GitHub stars~5.4k tokensUpdated 2 days ago
    Sales & SupportAuto-check: notes

  • Google Maps Export

    gmapsscraper/google-maps-agent-skills

    Export Google Maps business data to CSV, JSON, or CRM format (HubSpot, Pipedrive, Salesforce).

    132 GitHub stars~1.2k tokensUpdated 4 mo ago
    Sales & SupportAuto-check passed

More from Jaganpro/sf-skills

All 36 skills in this repo

  • Sf AI Agentforce Observability

    Jaganpro/sf-skills

    Agentforce session tracing extraction and analysis. An agent skill from Jaganpro/sf-skills.

    424 GitHub stars~1.8k tokensUpdated 5 mo ago
    Auto-check passed

  • Sf AI Agentscript

    Jaganpro/sf-skills

    Agent Script DSL for deterministic Agentforce agents. An agent skill from Jaganpro/sf-skills.

    424 GitHub stars~3.8k tokensUpdated 5 mo ago
    Auto-check passed

  • Sf Datacloud

    Jaganpro/sf-skills

    Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows.

    424 GitHub stars~2.7k tokensUpdated 5 mo ago
    Auto-check passed

  • Sf Diagram Mermaid

    Jaganpro/sf-skills

    Salesforce architecture diagrams using Mermaid with ASCII fallback.

    424 GitHub stars~1.4k tokensUpdated 5 mo ago
    Auto-check passed

  • Sf Diagram Nanobananapro

    Jaganpro/sf-skills

    AI-powered image generation for Salesforce visuals via Nano Banana Pro.

    424 GitHub stars~1.6k tokensUpdated 5 mo ago
    Auto-check passed

  • Sf Flow

    Jaganpro/sf-skills

    Creates and validates Salesforce Flows with 110-point scoring.

    424 GitHub stars~1.8k tokensUpdated 5 mo ago
    Auto-check passed

Works with

Categories

Questions about Sf Flex Estimator

What does Sf Flex Estimator do?

Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads. Sf Flex Estimator is an agent skill from Jaganpro/sf-skills. Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads.

When should I use Sf Flex Estimator?

Sf Flex Estimator fits situations like: : user needs cost projections; scenario planning; architecture tradeoff analysis for Agentforce prompts/actions; data Cloud meters.

How do I install Sf Flex Estimator in Claude Code?

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

How do I install Sf Flex Estimator in Codex?

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

Can I use Sf Flex Estimator 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 Jaganpro/sf-skills --skill sf-flex-estimator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sf-flex-estimator, .gemini/skills/sf-flex-estimator, .github/skills/sf-flex-estimator and .opencode/skills/sf-flex-estimator in your project.

What does Sf Flex Estimator need to run?

Going by SKILL.md and its folder, Sf Flex Estimator needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Sf Flex Estimator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Sf Flex Estimator 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. Review the folder before installing.

What licence does Sf Flex Estimator use?

Sf Flex Estimator is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sf Flex Estimator use?

About 2.1k tokens (SKILL.md is roughly 8.5k 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 11k tokens, read only when the agent opens those files.

What are the alternatives to Sf Flex Estimator?

Skills that share tags, products or a category with Sf Flex Estimator: Soql Lib Query Builder (beyond-the-cloud-dev/soql-lib, 154 stars), Soql Lib Selector (beyond-the-cloud-dev/soql-lib, 154 stars), Dev Setup (Portwood-Global-Solutions/Portwood, 126 stars) and Automation Sandbox Post Copy Configure (forcedotcom/sf-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sf Flex Estimator?

Jaganpro (a GitHub user) maintains it in Jaganpro/sf-skills, which has 424 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on April 27, 2026.

Source: Jaganpro/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.