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accelint-prompt-manager

Clarify underspecified user requests into structured prompts via a 4-phase workflow.

Updated Mar 24, 2026
Search Tags:#optimization#ai#clarification#framework#prompt#language-model
One-click install
npx skills add https://github.com/Ahnd6474/Jakal-flow --skill accelint-prompt-manager
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: accelint-prompt-manager
Source: https://github.com/Ahnd6474/Jakal-flow/tree/main/.skill-staging/skills/accelint-prompt-manager
Command: npx skills add https://github.com/Ahnd6474/Jakal-flow --skill accelint-prompt-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Many users struggle with underspecified, ambiguous, or vague requests that prevent AI models from delivering accurate results. This skill clarifies and refines such inputs into precise, actionable prompts that are ready for execution.

Core Features & Use Cases

  • Four‑phase workflow: Intake & assessment, pattern detection, framework selection & optimization, validation & handoff.
  • Anti‑pattern detection: Identifies credit‑killing patterns, ambiguities, and missing constraints.
  • Framework routing: Silently applies CO‑STAR, RISEN, or RODES structures without exposing methodology.
  • Safe optimization techniques: Specificity injection, constraint addition, context positioning, pronoun elimination, success‑criteria definition.
  • Plan mode recommendation: Automatically suggests planning for complex, multi‑phase tasks.
  • Optional post‑delivery actions: Save optimized prompt to a file or copy to clipboard.

Quick Start

Ask the skill to improve a vague request like “make this better” and it will guide you through clarifying questions, then deliver an optimized prompt in a markdown code block.

Frequently Asked Questions about accelint-prompt-manager

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn a vague request into a clear prompt for a language model?▼

Prompt optimization clarifies underspecified inputs by detecting anti-patterns like ambiguity or missing constraints, then applying safe optimization techniques such as specificity injection and context positioning to deliver a well-structured prompt ready for execution.

How do I fix ambiguous AI prompts before sending them to a language model?▼

Fix ambiguous AI prompts by running them through a four-phase workflow that detects credit-killing anti-patterns, eliminates pronouns, injects specificity, and defines success criteria to produce a validated, actionable prompt.

Does prompt optimization work for both coding and writing tasks?▼

Yes, prompt optimization works across any domain such as coding, writing, analysis, or documentation. The workflow applies framework routing using CO-STAR, RISEN, or RODES structures to clarify requests regardless of the subject matter.

What is the best way to structure a complex multi-phase task for AI?▼

The best way to structure a complex multi-phase task is to use a prompt optimization workflow that recommends plan mode, refining the request into a structured prompt with defined constraints and success criteria before execution begins.

Can I save optimized prompts to a file after generation?▼

Yes, you can save optimized prompts to a file or copy them to your clipboard. After the validation and handoff phase delivers the refined prompt in a markdown code block, optional post-delivery actions allow you to export the result.

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