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Skills Planner

CommunityPopular
dp-archive
skills-planner

Plan which skills are needed to fulfill user requirements. Use when the user wants to design an agent workflow, plan skill composition, or determine what skills are needed for a task. Input includes user requirements and existing skills list. Output includes recommended existing skills, new skills to create, and a system prompt for the composed agent.

Overview

Publisherdp-archive
Repositoryarchive
Skill nameskills-planner
Stars
1.1K
Forks
100
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dp-archive on GitHub. Read the source before you install it.

Installation

Install the Skills Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dp-archive/archive/tree/main/seed_skills/skills-planner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dp-archive/archive.git /tmp/archive
mkdir -p .claude/skills
cp -r /tmp/archive/seed_skills/skills-planner .claude/skills/skills-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skills Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skills Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skills Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skills Planner

Plan and compose skills to fulfill user requirements efficiently.

Workflow

  1. Analyze Requirements - Understand user goals and constraints
  2. Evaluate Existing Skills - Match requirements to available skills
  3. Identify Gaps - Determine what capabilities are missing
  4. Design New Skills - Specify requirements for missing skills (if any)
  5. Compose Agent - Design system prompt for the agent using all skills
  6. Present Plan - Output structured plan for user confirmation

Input Requirements

Before planning, gather:

  • User Requirements: What the user wants to accomplish
  • Existing Skills List: Available skills with their descriptions

Planning Process

Step 1: Analyze Requirements

Break down user requirements into discrete capabilities:

User Request: "Build an agent that can analyze financial reports"

Required Capabilities:
1. PDF reading/parsing
2. Data extraction (tables, numbers)
3. Financial metrics calculation
4. Report generation
5. Visualization (charts)

Step 2: Evaluate Existing Skills

For each capability, check if an existing skill covers it:

Capability: PDF reading/parsing
Existing Skill Match: pdf (extracts text/tables from PDFs) ✓

Capability: Financial metrics calculation
Existing Skill Match: None found ✗

Selection Criteria:

  • Prefer skills that directly match the capability
  • Consider skill scope (narrow and focused > broad and generic)
  • Check for overlapping functionality to avoid redundancy

Step 3: Identify Gaps

List capabilities not covered by existing skills. These become candidates for new skills.

Gap Analysis Questions:

  • Is this capability truly necessary, or can existing skills be combined?
  • Can the base model handle this without a skill?
  • Is this capability reusable across other tasks?

Step 4: Design New Skills (if needed)

For each gap, specify:

yaml
skill_name: financial-analyzer
skill_requirements: |
  Purpose: Calculate and interpret financial metrics from extracted data

  Core Capabilities:
  - Calculate common ratios (P/E, ROE, debt-to-equity, etc.)
  - Identify trends across time periods
  - Flag anomalies or concerns
  - Generate insights in plain language

  Input: Structured financial data (revenue, expenses, assets, etc.)
  Output: Analysis report with metrics, trends, and recommendations

  Workflow:
  1. Validate input data completeness
  2. Calculate standard financial ratios
  3. Compare to industry benchmarks (if provided)
  4. Identify significant changes or outliers
  5. Generate narrative summary

Principles for New Skills:

  • Minimal: Only create skills that provide significant value
  • Focused: Each skill should do one thing well
  • Reusable: Design for use beyond the immediate task
  • Claude-aware: Only include knowledge Claude doesn't already have

Step 5: Compose Agent System Prompt

Design a system prompt that orchestrates all selected skills:

markdown
## System Prompt Template

You are a [AGENT_ROLE] agent specialized in [DOMAIN].

### Workflow
When processing requests:
1. [First step - e.g., understand the input]
2. [Second step - e.g., invoke relevant skill]
3. [Third step - e.g., process results]
4. [Final step - e.g., present output]

### Guidelines
- [Guideline 1 - e.g., always validate inputs first]
- [Guideline 2 - e.g., handle errors gracefully]
- [Guideline 3 - e.g., provide clear explanations]

### Output Format
[Specify expected output structure if applicable]

Output Format

Present the plan in this structure:

markdown
# Skills Plan for [User Task]

## 1. Existing Skills to Include

| Skill Name | Purpose in This Agent | Key Capabilities Used |
|------------|----------------------|----------------------|
| [name] | [why needed] | [specific features] |
| [name] | [why needed] | [specific features] |

> If no existing skills apply, write: "None - all required capabilities need new skills."

## 2. New Skills to Create

**Skills needed**: [skill-a], [skill-b], [skill-c] (or "None - existing skills cover all requirements")

### 2.1 [skill-a]

(Continue for each new skill...)

---

## 3. Proposed System Prompt

[Complete system prompt for the agent]

---

## 4. Additional Recommendations

- [Recommendation 1 - e.g., consider adding X capability later]
- [Recommendation 2 - e.g., this skill combination may have limitation Y]
- [Recommendation 3 - e.g., suggested testing approach]

---

**Please review and confirm this plan, or provide feedback for adjustments.**

Example

User Request: "I need an agent that can process customer support tickets, categorize them, and draft responses"

Existing Skills: web-search, email-sender, sentiment-analyzer

Output:

markdown
# Skills Plan for Customer Support Agent

## 1. Existing Skills to Include

| Skill Name | Purpose in This Agent | Key Capabilities Used |
|------------|----------------------|----------------------|
| sentiment-analyzer | Assess ticket urgency and customer mood | Sentiment scoring, emotion detection |

## 2. New Skills to Create

**Skills needed**: ticket-categorizer, response-drafter

### 2.1 ticket-categorizer

**Purpose**: Classify support tickets into predefined categories

**Requirements**:
- Parse ticket content (subject + body)
- Match against category definitions
- Handle multi-category tickets
- Provide confidence scores

**Workflow**:
1. Extract key phrases from ticket
2. Match against category keywords and patterns
3. Assign primary and secondary categories
4. Return categorization with confidence

**Input/Output**:
- Input: Ticket text (subject, body, metadata)
- Output: { category: string, subcategory: string, confidence: float }

### 2.2 response-drafter

**Purpose**: Generate contextual draft responses for support tickets

**Requirements**:
- Use ticket category and sentiment as context
- Apply company tone guidelines
- Include relevant knowledge base references
- Support multiple response templates

**Workflow**:
1. Analyze ticket intent and key issues
2. Select appropriate response template
3. Customize with specific details
4. Add relevant links/resources
5. Apply tone adjustments based on sentiment

**Input/Output**:
- Input: Ticket content, category, sentiment, knowledge base context
- Output: Draft response text with placeholders for review

---

## 3. Proposed System Prompt

You are a Customer Support Agent specialized in processing and responding to support tickets efficiently.

### Your Capabilities
You have access to the following skills:
- **sentiment-analyzer**: Use to assess customer mood and ticket urgency before categorizing
- **ticket-categorizer**: Use to classify tickets into appropriate categories
- **response-drafter**: Use to generate initial response drafts

### Workflow
When processing a support ticket:
1. Analyze sentiment to understand customer mood and urgency
2. Categorize the ticket to determine the issue type
3. Draft an appropriate response based on category and sentiment
4. Present the draft for human review before sending

### Guidelines
- Prioritize high-urgency tickets (negative sentiment + billing/account issues)
- Always maintain a helpful, empathetic tone
- Escalate complex technical issues to specialists
- Include relevant self-help resources when applicable

### Output Format
For each ticket, provide:
- Sentiment Assessment: [score and summary]
- Category: [primary] / [secondary if applicable]
- Urgency: [Low/Medium/High]
- Draft Response: [response text]
- Recommended Actions: [any escalation or follow-up needed]

---

## 4. Additional Recommendations

- Consider adding a knowledge-base-search skill for FAQ matching
- The ticket-categorizer should be trained on historical ticket data
- Test with edge cases: multi-issue tickets, non-English tickets, spam
- Monitor response quality and adjust tone guidelines as needed

---

**Please review and confirm this plan, or provide feedback for adjustments.**

Key Principles

  1. Minimize skill count: Fewer, well-designed skills > many narrow skills
  2. Leverage Claude's base capabilities: Don't create skills for things Claude already knows
  3. Prefer existing skills: Only create new skills when truly necessary
  4. Design for reusability: New skills should be useful beyond the immediate task
  5. Clear boundaries: Each skill should have a distinct, non-overlapping purpose

Frequently asked questions

What does the Skills Planner AI skill do?

Plan which skills are needed to fulfill user requirements. Use when the user wants to design an agent workflow, plan skill composition, or determine what skills are needed for a task. Input includes user requirements and existing skills list. Output includes recommended existing skills, new skills to create, and a system prompt for the composed agent.

Why use Skills Planner on TypingMind?

Because you install it once and use it with any model. Skills Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skills Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dp-archive/archive/tree/main/seed_skills/skills-planner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skills Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skills Planner?

As many as you like. As long as a model supports skills, you can use Skills Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skills Planner AI skill free?

Yes. It is published on GitHub by dp-archive under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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