fluxly Search skills… ⌘K 中文 Publish

sales-talkdesk

Engineering Verified v1.0.0
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Engineering · sales · contact-center · ccaas
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
100 / 100 · audit passed
Author / version / license
@sales-skills · v1.0.0 · MIT
Token usage
Lean
Setup complexity
Guided setup
External API key
Not required
Operating systems
macOS · Linux · Windows
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
  • Shell exec
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Output preview sales-talkdesk.preview 
---
name: sales-talkdesk
description: Talkdesk platform help — cloud contact center (CCaaS) with AI virtual agents, omnichannel routin…
category: engineering
runtime: no special runtime
---

# sales-talkdesk output preview

## PART A: Task fit
- Use case: Talkdesk platform help — cloud contact center (CCaaS) with AI virtual agents, omnichannel routing, workforce management, and quality management. Use when setting up Talkdesk ACD routing or Studio IVR, calls keep dropping or audio quality is bad, AI features like Autopilot and CoPilot are expensive add-ons, comparing Talkdesk pricing tiers (Digital $85 to Industry $225/agent/mo), integrating Talkdesk with Salesforce Service Cloud Voice, Talkdesk reporting is hard to customize, WFM forecasting or scheduling not working, or implementation taking too long. Do NOT use for building a general coaching program (use /sales-coaching) or reviewing a specific call transcript (use /sales-call-review)..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Step 1 — Gather context / Step 2 — Route or answer directly / Step 3 — Talkdesk platform reference” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Talkdesk platform help — cloud contact center (CCaaS) with AI virtual agents, omnichannel routing, workforce management, and quality management. Use when setting up Talkdesk ACD routing or Studio IVR, calls keep dropping or audio quality is bad, AI features like Autopilot and CoPilot are expensive add-ons, comparing Talkdesk pricing tiers (Digital $85 to Industry $225/agent/mo), integrating Talkdesk with Salesforce Service Cloud Voice, Talkdesk reporting is hard to customize, WFM forecasting or scheduling not working, or implementation taking too long. Do NOT use for building a general coaching program (use /sales-coaching) or reviewing a specific call transcript (use /sales-call-review).”.
- **02** When the source has headings, the agent prioritizes “Step 1 — Gather context / Step 2 — Route or answer directly / Step 3 — Talkdesk platform reference” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; may access external network resources; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Talkdesk platform help — cloud contact center (CCaaS) with AI virtual agents, omnichannel routing, workforce management, and qua…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Step 1 — Gather context”, “Step 2 — Route or answer directly”, “Step 3 — Talkdesk platform reference”, “Step 4 — Actionable guidance”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name sales-talkdesk directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Step 1 — Gather context / Step 2 — Route or answer directly / Step 3 — Talkdesk platform reference” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

Powered by GitHub Discussions. Sign in with GitHub to comment, react, or subscribe.