How do I install the Overnight Research Loop skill?

Run npx skillmds@latest add knuckles-team/overnight-research-loop 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 Overnight Research Loop 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 Overnight Research Loop?

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 Overnight Research Loop free to use?

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

Who published Overnight Research Loop?

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

Overnight Research Loop by knuckles-team

Name: Overnight Research Loop
Author: Knuckles-Team

File contents

Overnight Research Loop Workflow

CONCEPT:EE-011

Runs overnight crawling of news/publications to generate factor hypotheses, runs backtests in parallel, debates findings, and generates a morning briefing.

Steps

Step 1: Hypothesis Crawlers

Agent: data-fetcher Tools: graph_query, sx_search

Scans academic publications, social feeds, and market tickers overnight to find potential factor ideas. Expected: factor-hypothesis-list

Step 2: Parallel Backtester [depends_on: hypothesis-crawlers]

Agent: compute-engine Tools: graph_analyze

Runs backtests concurrently across 50 parameter configurations. Expected: multi-config-backtest-results

Step 3: Factor Debater [depends_on: parallel-backtester]

Agent: risk-assessor Tools: graph_query, graph_analyze

Analyzes return profile, drawdown, and transaction cost impact in a swarm debate. Expected: approved-factor-signals

Step 4: Report Compiler [depends_on: factor-debater]

Agent: report-generator Tools: graph_write, document_tools

Compiles findings into a formatted dashboard report for the morning review. Expected: morning-research-tearsheet

Step 5: KG Persistence [depends_on: report-compiler]

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

Overnight Research Loop 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 — Hypothesis Crawlers
After level 0: Step 2 — Parallel Backtester
After level 1: Step 3 — Factor Debater
After level 2: Step 4 — Report Compiler
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: overnight-research-loopdescription: Runs overnight crawling of news/publications to generate factor hypotheses, runs backtests in parallel, debates findings, and generates a morning briefing.---# Overnight Research Loop Workflow**CONCEPT:EE-011**Runs overnight crawling of news/publications to generate factor hypotheses, runs backtests in parallel, debates findings, and generates a morning briefing.## Steps### Step 1: Hypothesis Crawlers**Agent**: `data-fetcher`**Tools**: `graph_query, sx_search`Scans academic publications, social feeds, and market tickers overnight to find potential factor ideas.Expected: `factor-hypothesis-list`### Step 2: Parallel Backtester [depends_on: hypothesis-crawlers]**Agent**: `compute-engine`**Tools**: `graph_analyze`Runs backtests concurrently across 50 parameter configurations.Expected: `multi-config-backtest-results`### Step 3: Factor Debater [depends_on: parallel-backtester]**Agent**: `risk-assessor`**Tools**: `graph_query, graph_analyze`Analyzes return profile, drawdown, and transaction cost impact in a swarm debate.Expected: `approved-factor-signals`### Step 4: Report Compiler [depends_on: factor-debater]**Agent**: `report-generator`**Tools**: `graph_write, document_tools`Compiles findings into a formatted dashboard report for the morning review.Expected: `morning-research-tearsheet`### Step 5: KG Persistence [depends_on: report-compiler]**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- Overnight Research Loop 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 — Hypothesis Crawlers- **After level 0:** Step 2 — Parallel Backtester- **After level 1:** Step 3 — Factor Debater- **After level 2:** Step 4 — Report Compiler- **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

Overnight Research Loop