How do I install the Regime Detection Pipeline skill?

Run npx skillmds@latest add knuckles-team/regime-detection-pipeline in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Regime Detection Pipeline safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Regime Detection Pipeline?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Regime Detection Pipeline free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Regime Detection Pipeline?

Knuckles-Team (@knuckles-team) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Regime Detection Pipeline by knuckles-team

Name: Regime Detection Pipeline
Author: Knuckles-Team

File contents

Regime Detection Pipeline Workflow

CONCEPT:EE-011

Collects pricing indicators, extracts structural features, fits Hidden Markov Model classifiers, and adapts strategy params to structural shifts.

Steps

Step 1: Data Collector

Agent: data-fetcher Tools: graph_query, sx_search

Collects tick volumes, bid-ask spreads, and rolling volatilities. Expected: market-regime-raw-data

Step 2: Feature Extractor [depends_on: data-collector]

Agent: compute-engine Tools: graph_analyze

Normalizes features and computes rolling volatility and trend indicators. Expected: structured-regime-features

Step 3: Hmm Regime Fitter [depends_on: feature-extractor]

Agent: risk-assessor Tools: graph_query, graph_analyze

Fits a Hidden Markov Model (HMM) to classify market state. Expected: hmm-state-classifications

Step 4: Regime Adapter [depends_on: hmm-regime-fitter]

Agent: report-generator Tools: graph_write, document_tools

Triggers strategy parameters modification based on the detected state. Expected: adapted-parameters-configs

Step 5: KG Persistence [depends_on: regime-adapter]

Agent: report-generator Tools: graph_write

Persist workflow results as nodes and edges in the Knowledge Graph. Create appropriate typed nodes with metadata and link to existing domain entities.

Output

Regime Detection Pipeline results persisted in KG
Structured report (MD/PDF)
Audit trail with timestamps and agent attributions

Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.

Run first (in parallel): Step 1 — Data Collector
After level 0: Step 2 — Feature Extractor
After level 1: Step 3 — Hmm Regime Fitter
After level 2: Step 4 — Regime Adapter
After level 3: Step 5 — KG Persistence

Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.

---name: regime-detection-pipelinedescription: Collects pricing indicators, extracts structural features, fits Hidden Markov Model classifiers, and adapts strategy params to structural shifts.---# Regime Detection Pipeline Workflow**CONCEPT:EE-011**Collects pricing indicators, extracts structural features, fits Hidden Markov Model classifiers, and adapts strategy params to structural shifts.## Steps### Step 1: Data Collector**Agent**: `data-fetcher`**Tools**: `graph_query, sx_search`Collects tick volumes, bid-ask spreads, and rolling volatilities.Expected: `market-regime-raw-data`### Step 2: Feature Extractor [depends_on: data-collector]**Agent**: `compute-engine`**Tools**: `graph_analyze`Normalizes features and computes rolling volatility and trend indicators.Expected: `structured-regime-features`### Step 3: Hmm Regime Fitter [depends_on: feature-extractor]**Agent**: `risk-assessor`**Tools**: `graph_query, graph_analyze`Fits a Hidden Markov Model (HMM) to classify market state.Expected: `hmm-state-classifications`### Step 4: Regime Adapter [depends_on: hmm-regime-fitter]**Agent**: `report-generator`**Tools**: `graph_write, document_tools`Triggers strategy parameters modification based on the detected state.Expected: `adapted-parameters-configs`### Step 5: KG Persistence [depends_on: regime-adapter]**Agent**: `report-generator`**Tools**: `graph_write`Persist workflow results as nodes and edges in the Knowledge Graph.Create appropriate typed nodes with metadata and link to existing domain entities.## Output- Regime Detection Pipeline results persisted in KG- Structured report (MD/PDF)- Audit trail with timestamps and agent attributions## ExecutionRun this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.- **Run first (in parallel):** Step 1 — Data Collector- **After level 0:** Step 2 — Feature Extractor- **After level 1:** Step 3 — Hmm Regime Fitter- **After level 2:** Step 4 — Regime Adapter- **After level 3:** Step 5 — KG Persistence**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.

Frequently asked questions

Regime Detection Pipeline