Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add BanibrataChatterjee/AwesomeSalesforceSkills --skill fsl-scheduling-policiesgit clone --depth 1 https://github.com/BanibrataChatterjee/AwesomeSalesforceSkillsWrote 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/banibratachatterjee/awesomesalesforceskills/fsl-scheduling-policies)<a href="https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/fsl-scheduling-policies"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/fsl-scheduling-policies/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/banibratachatterjee/awesomesalesforceskills/fsl-scheduling-policies"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/fsl-scheduling-policies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00080 | $0.03545 |
| Opus 5 | $0.00040 | $0.01773 |
| Sonnet 5 | $0.00016 | $0.00709 |
| Haiku 4.5 | $0.00008 | $0.00354 |
Grade A, and why
fsl-scheduling-policies scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FSL Scheduling Policies
This skill activates when a practitioner needs to create, modify, or troubleshoot a Field Service Lightning scheduling policy — the configuration object that governs how the scheduling engine filters and ranks available service resources for appointment slots. It covers work rule design, service objective weighting, and the selection between Salesforce's four built-in default policies.
Before Starting
Gather this context before working on anything in this domain:
- Confirm Field Service managed package is installed and Field Service is enabled in Setup > Field Service Settings. The FSL__Scheduling_Policy__c, FSL__Work_Rule__c, and FSL__Service_Objective__c objects must be accessible.
- Identify whether the org is using the Salesforce optimizer (bulk scheduling) or dispatcher-driven manual scheduling, or both. Policy behavior is the same in both cases, but the consequences of misconfiguration are more visible in bulk optimization runs.
- Determine the primary business priority: customer appointment windows, minimizing travel cost, emergency SLA compliance, or contractor overtime control. This drives which default policy to start from and which objectives to weight most heavily.
- Know the complete set of required skills and certifications defined in the org, because Match Skills and Match Required Skills work rules depend on this data being populated on resource records.
- Note that every custom policy MUST include a Service Resource Availability work rule. Without it, the scheduling engine completely ignores resource working hours and absences during candidate evaluation.
Core Concepts
FSL__Scheduling_Policy__c and Its Two Child Object Types
FSL__Scheduling_Policy__c is the top-level scheduling policy object. It acts as a container for two categories of child configuration records:
- Work Rules (
FSL__Work_Rule__c) — hard pass/fail filters. A candidate time slot is eliminated from consideration if it violates any active work rule. Work rules are binary: the slot either passes or it does not. There is no partial credit.
What ships with it
6 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.
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 First seen · 237 lines · 80 tokens per session scan A 7163e6b44c73
fsl-scheduling-policies is a skill published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 3,545 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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