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Progressive Discovery Skill

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zeenie-ai
progressive-discovery-skill

Discover and load tools progressively as needed (reduces token overhead)

Overview

Publisherzeenie-ai
RepositoryOpenCompany
Skill nameprogressive-discovery-skill
Stars
912
Forks
137
Bundled files
Instructions only
LicenseMIT
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 zeenie-ai on GitHub. Read the source before you install it.

Installation

Install the Progressive Discovery Skill 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.
  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/zeenie-ai/OpenCompany.git /tmp/OpenCompany
mkdir -p .claude/skills
cp -r /tmp/OpenCompany/server/skills/autonomous/progressive-discovery-skill .claude/skills/progressive-discovery-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Progressive Discovery Skill 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 Progressive Discovery Skill 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 Progressive Discovery Skill 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.

Progressive Tool Discovery

You are an agent that discovers and uses capabilities progressively as needed, rather than loading everything upfront.

Why Progressive Discovery?

Loading all tools at start creates:

  • Token overhead: 150,000+ tokens for tool definitions
  • Context pollution: Irrelevant tools confuse the LLM
  • Slower responses: More tokens = more processing time

Progressive discovery provides:

  • 98.7% token savings (Anthropic MCP research)
  • Focused context: Only relevant tools loaded
  • Better decisions: Less noise, clearer choices

Discovery Pattern

┌─────────────────────────────────────────────────────────────┐
│                 PROGRESSIVE DISCOVERY                        │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│   1. START with minimal context                             │
│         │                                                   │
│         ▼                                                   │
│   2. IDENTIFY what capability is needed                     │
│         │                                                   │
│         ▼                                                   │
│   3. CHECK what tools/agents are connected                  │
│         │                                                   │
│         ├──▶ Tool exists? ──▶ USE it directly              │
│         │                                                   │
│         └──▶ Specialized agent exists? ──▶ DELEGATE to it  │
│                                                              │
│   4. EXECUTE with focused context                           │
│         │                                                   │
│         ▼                                                   │
│   5. RETURN result (don't load more than needed)           │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Available Specialized Agents

Check your connected tools - you may have access to these specialized agents:

AgentCapabilitiesWhen to Delegate
android_agentDevice control, apps, sensorsAndroid device tasks
coding_agentPython/JavaScript executionCode computation
web_agentWeb scraping, HTTP requestsInternet data
task_agentScheduling, timers, cronTime-based tasks
social_agentWhatsApp, messagingCommunication
travel_agentMaps, locations, directionsTravel planning

Discovery Examples

Example 1: User asks "What's the battery level?"

Without Progressive Discovery (wasteful):

Load ALL tools (calculator, web_search, whatsapp, android, maps, code, ...)
Parse user request
Find that android tool is needed
Execute battery check

With Progressive Discovery (efficient):

1. IDENTIFY: This needs Android device access
2. CHECK: Is android_agent or battery tool connected?
3. YES → Delegate: "Check battery level"
4. RETURN: Battery at 75%, charging

Example 2: User asks "Calculate compound interest for $10,000 at 5% for 10 years"

Discovery Process:

1. IDENTIFY: This is a mathematical calculation
2. CHECK: Do I have python_code or calculator tool?

   IF python_code connected:
      Use Code Mode for complex calculation

   ELIF calculator connected:
      Use calculator for simple operations

   ELSE:
      Report: "I need a code executor or calculator to compute this"

Example 3: User asks "Send my location to John on WhatsApp"

Discovery Process:

1. IDENTIFY: Needs location + messaging
2. CHECK: What's connected?

   Step A - Get location:
   IF location tool connected → Use directly
   ELIF android_agent connected → Delegate location request
   ELSE → Ask user for location

   Step B - Send message:
   IF whatsapp_send connected → Use directly
   ELIF social_agent connected → Delegate message
   ELSE → Report: "WhatsApp not available"

Delegation Pattern

When delegating to a specialized agent:

json
{
  "task": "Specific task description",
  "context": "Relevant context only (not everything)"
}

Good Context (focused):

json
{
  "task": "Get current GPS coordinates",
  "context": "User needs their location for a WhatsApp message"
}

Bad Context (bloated):

json
{
  "task": "Get current GPS coordinates",
  "context": "Full conversation history... user preferences... all previous results... system info..."
}

Capability Check Pattern

Before attempting an action, verify the capability exists:

IF task requires capability X:

    IF direct_tool_for_X is connected:
        → Use tool directly (fastest)

    ELIF specialized_agent_for_X is connected:
        → Delegate to agent (handles complexity)

    ELSE:
        → Report: "This capability is not available"
        → Suggest: "Connect [tool/agent name] to enable this"

Anti-Patterns to Avoid

1. Loading Everything Upfront

❌ "Let me check all my tools: calculator, web_search, whatsapp,
    android, maps, code, http, scheduler, memory..."

✓ "To answer this, I need [specific capability]"

2. Delegating Without Checking

❌ Immediately delegate to android_agent without checking if connected

✓ Check connected tools first, then delegate if available

3. Over-Explaining Capabilities

❌ "I have access to many tools including... [lists everything]"

✓ "I can help with that. Let me [specific action]."

4. Redundant Delegation

❌ Delegate "calculate 2+2" to coding_agent

✓ Simple math can be done directly or with calculator tool

Integration with Agentic Loop

Progressive Discovery works with the Agentic Loop pattern:

OBSERVE: What does the user need?
THINK: What capability is required?
DISCOVER: Is that capability connected?
ACT: Use tool directly OR delegate to agent
REFLECT: Did it work?
DECIDE: Complete or discover next capability

Best Practices

  1. Start minimal - Don't enumerate all tools at the start
  2. Discover on demand - Only check for capabilities when needed
  3. Prefer direct tools - Use connected tools before delegating
  4. Focused delegation - Pass only relevant context to agents
  5. Report gaps clearly - If capability missing, say what's needed

Frequently asked questions

What does the Progressive Discovery Skill AI skill do?

Discover and load tools progressively as needed (reduces token overhead)

Why use Progressive Discovery Skill on TypingMind?

Because you install it once and use it with any model. Progressive Discovery Skill 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 Progressive Discovery Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zeenie-ai/OpenCompany/tree/main/server/skills/autonomous/progressive-discovery-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Progressive Discovery Skill?

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 Progressive Discovery Skill?

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

Is the Progressive Discovery Skill AI skill free?

Yes. It is published on GitHub by zeenie-ai under the MIT 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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