# Decker

> Decker — gigshow-decker-ai. Use this tool when you need to analyze and predict market trends with precision, as it provides a deterministic market-state engine for trading agents. Decker solves problems related to trading decision-making by offering features such as structural market state analysis, action gates, and entry/target/invalidation coordinates. It takes in market data and outputs actionable trading signals, making it ideal for use in high-stakes trading environments where accuracy and transparency are crucial.

Canonical page: https://skillsregistry.net/skills/gigshow-decker-ai  
JSON: https://api.skillsregistry.net/v1/skills/gigshow-decker-ai

## Description

Deterministic market-state engine for trading agents — zero LLM in the signal path. 8 tools: structural market state & phase, action gate (GO/WATCH/HOLD), entry/target/invalidation coordinates, bar-by-bar state timeline, composed view cards, and pre-trade intent validation. Every output traces to a bar-stamped ledger with a public daily self-scoring track record.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** finance
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/kcjp7nszpu)
- **Repository:** <https://github.com/gigshow/decker-ai>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "gigshow-decker-ai"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gigshow-decker-ai` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gigshow-decker-ai/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt