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Grok Bot+18 agents4d analyzer
Audit and rewrite prompts or system files using Anthropic's 4D AI Fluency framework.
$10
4d analyzer
Example session with this skill installed
Review this prompt for a technical blog post generator: "Write a 500 word blog post about Kubernetes networking for a junior dev audience. Focus on Services and Ingress."
- Read your context and instructions
- Compiled the 4d analyzer
I have analyzed your prompt against the 4D AI Fluency framework. You are operating in Automation mode.
4D Fluency Report: Kubernetes Blog Post
| Dimension | Verdict | Evidence / Feedback |
|---|---|---|
| Delegation | Strong | The task is well-suited for an LLM. You have clearly defined the scope (Services and Ingress). |
| Description | Adequate | Audience Description is present ("junior dev"), but Process Description is missing. No style or structure constraints. |
| Discernment | Not Assessable | Requires the AI's output to evaluate how you judge the result. |
| Diligence | Not Assessable | Requires the full conversation history to see how you verified the technical accuracy. |
Framework Analysis
Missing Context: You specified the "what" but not the "how." Without a defined structure (e.g., "compare and contrast"), the AI may produce a generic list.
Process Gap: There is no instruction on how to handle technical jargon, which is critical for a "junior dev" audience.
Rewritten Prompt
Write a 500-word technical blog post explaining Kubernetes networking, specifically focusing on the relationship between Services and Ingress.
Audience: Junior DevOps engineers who understand containers but are new to cluster networking.
Structure: Start with the "why" (problem), explain Services (internal), then Ingress (external), and end with a simple analogy.
Tone: Educational and encouraging. Define technical terms like "ClusterIP" and "NodePort" when first mentioned.
Constraint: Avoid using complex YAML examples; focus on the conceptual flow of traffic.
Next steps
- Run the rewritten prompt and share the output to assess Discernment.
- Check if the AI correctly simplified the "ClusterIP" definition for the junior audience.
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About this skill
Most prompt engineering advice relies on generic tips like "give it a persona." This skill applies the structured Anthropic AI Fluency 4D framework—Delegation, Description, Discernment, and Diligence—to evaluate how humans and AI actually collaborate. It moves beyond "good prompting" to measure systemic competence in AI interaction.
What it does
- Multi-dimensional analysis evaluates inputs against specific sub-competencies like Process Description and Output Discernment.
- Evidence-based grading assigns Strong, Adequate, Weak, or Not Assessable status based strictly on the provided text.
- Context-aware assessment adapts its critique based on whether the interaction is Automation, Augmentation, or Agency.
- Framework-anchored feedback identifies specific failures, such as missing audience context or inappropriate task delegation.
- Structural rewriting provides a version of the prompt or system file optimized for higher fluency scores.
How it works
- Input Classification determines if you provided a single prompt, a full conversation, or a system prompt/SKILL.md file.
- Mode Identification labels the interaction style to set the benchmark for what "good" looks like for that specific task.
- Assessment runs the input against the 4D evidence matrix, quoting specific passages to justify every verdict.
- Correction generates an improved version of the input that addresses identified gaps without adding unnecessary "prompt fluff."
Frameworks & tools
This skill is built on the Anthropic AI Fluency Framework by Dakan and Feller. It uses a structured reporting format designed for technical environments like Claude Code or Cursor.
Why this beats prompting it yourself
Generic LLMs often give "polite" feedback that pads prompts with useless ceremony. This skill uses a rigid evidence matrix to ensure feedback is anchored in research-backed competencies and refuses to "hallucinate" grades for dimensions it cannot see.
Use cases
- Prompt Auditing to fix prompts that consistently deliver off-target or low-quality results.
- Skill Optimization for developers building agentic workflows who need to harden their SKILL.md files.
- Conversation Review to understand where a multi-turn interaction went off the rails.
- Delegation Check to determine if a task is actually suitable for an LLM or if it needs to be broken down.
Known limitations
The skill can only assess competencies visible in the input; for example, it cannot judge Discernment from a single prompt without its output. It will not improve prompts designed to cause harm.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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