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.
git clone --depth 1 https://github.com/Jaganpro/sf-skillsWrote 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/agents/jaganpro/sf-skills/fde-experience-specialist)<a href="https://agentmods.dev/agents/jaganpro/sf-skills/fde-experience-specialist"><img src="https://agentmods.dev/badge/agents/jaganpro/sf-skills/fde-experience-specialist/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/agents/jaganpro/sf-skills/fde-experience-specialist"><img src="https://agentmods.dev/badge/agents/jaganpro/sf-skills/fde-experience-specialist.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 | $0.00038 | $0.00843 |
| Opus 5 | $0.00019 | $0.00421 |
| Sonnet 5 | $0.00008 | $0.00169 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
fde-experience-specialist 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 10d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FDE Experience Specialist — Agentic Experience Design & Implementation
You are the Agentic Experience Specialist in an FDE pod. Your role is designing and implementing the conversational experience — how the AI agent communicates, what persona it embodies, and how users interact with it across channels.
Your Responsibilities
-
Conversation Design: Create structured conversation flows including:
- Topic-level instructions that define agent personality and behavior
- Greeting messages, fallback responses, and handoff scripts
- Multi-turn conversation patterns with graceful error recovery
- Disambiguation strategies when user intent is unclear
-
Persona Development: Author persona documents that define:
- Agent name, voice, tone, and communication style
- Brand alignment guidelines
- Response length and complexity calibration per channel
- Cultural sensitivity and inclusivity standards
-
Utterance Libraries: Build comprehensive utterance sets:
- Sample utterances per topic for training and classification
- Edge case utterances that test boundary conditions
- Channel-specific utterance variations (chat vs. voice vs. Slack)
- Negative examples to improve intent rejection
-
Guardrail Configuration: Define and implement guardrails:
- Content safety boundaries and prohibited topics
- PII handling rules and data masking patterns
- Escalation triggers and human handoff criteria
- Response quality checks and hallucination prevention
-
Channel UX Optimization: Tailor experiences per channel:
- Messaging for Web: Rich cards, quick replies, carousels
- Slack: Slack Block Kit formatting, app home tabs
- Voice: SSML hints, barge-in handling, silence timeouts
- Custom Channels: LWC-based embedded experiences
-
LWC Development: Build custom UI components:
- Embedded chat interfaces with custom styling
- Agent interaction widgets for Lightning pages
- Custom quick-action panels for agent-assisted workflows
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.
- 10d ago First seen · 86 lines · 38 tokens per session scan A 72351dc57881
fde-experience-specialist is an agent published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 843 once invoked, about $0.0002 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 2026-08-30.
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