How do I install the Sentiment Alpha Project skill?

Run npx skillmds@latest add knuckles-team/sentiment-alpha-project 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.

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Which AI agents work with Sentiment Alpha Project?

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 Sentiment Alpha Project free to use?

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Who published Sentiment Alpha Project?

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

Sentiment Alpha Project by knuckles-team

Name: Sentiment Alpha Project
Author: Knuckles-Team

Sentiment Alpha Project

Build the sentiment-alpha portfolio project: scrape financial news headlines (NewsAPI free tier), score them with FinBERT, construct a sentiment signal, backtest it leak-free, and publish a GitHub repo.

Knuckles-Team ded0076 2 files · 2.9 KB Updated Sep 17, 2026

File contents

Sentiment Alpha Project Workflow

Project 4 of the quant portfolio. Turn news sentiment into a tradable signal. See quant-career-docs/reference/ml-for-finance.md.

Steps

Step 1: scrape-news

Agent: quant_developer Tools: data-science-mcp

Pull financial news headlines for target tickers from the NewsAPI free tier. Timestamp each headline for point-in-time alignment (avoid lookahead). Expected: timestamped-headlines

Step 2: finbert-score [depends_on: Step 1]

Agent: quant_researcher

Run FinBERT over the headlines to produce per-headline sentiment scores (positive/negative/neutral). Expected: sentiment-scores

Step 3: build-signal [depends_on: Step 2]

Agent: quant_researcher

Aggregate sentiment into a daily per-ticker signal; define the mapping from sentiment to position (long/flat/short). Expected: sentiment-signal

Step 4: backtest [depends_on: Step 3]

Agent: quant_developer

Backtest the sentiment signal with realistic fees/slippage; audit for lookahead bias. Report Sharpe, max drawdown, CAGR vs benchmark. Expected: backtest-metrics

Step 5: github-publish [depends_on: Step 4]

Agent: quant_developer

Publish a GitHub repo with README, metrics, clean code, and an honest "what didn't work" section. Expected: github-repo-url

Step 6: kg-persist [depends_on: Step 5]

Agent: quant_researcher Tools: graph_write

Persist the project and metrics as typed nodes linked to the portfolio.

Output

A FinBERT-driven sentiment-alpha signal, backtested honestly
A published GitHub portfolio repo

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 — scrape-news
After level 0: Step 2 — finbert-score
After level 1: Step 3 — build-signal
After level 2: Step 4 — backtest
After level 3: Step 5 — github-publish
After level 4: Step 6 — kg-persist

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: sentiment-alpha-projectdescription: Build the sentiment-alpha portfolio project: scrape financial news headlines (NewsAPI free tier), score them with FinBERT, construct a sentiment signal, backtest it leak-free, and publish a GitHub repo.---# Sentiment Alpha Project WorkflowProject 4 of the quant portfolio. Turn news sentiment into a tradable signal. See`quant-career-docs/reference/ml-for-finance.md`.## Steps### Step 1: scrape-news**Agent**: `quant_developer`**Tools**: `data-science-mcp`Pull financial news headlines for target tickers from the NewsAPI free tier.Timestamp each headline for point-in-time alignment (avoid lookahead).Expected: `timestamped-headlines`### Step 2: finbert-score [depends_on: Step 1]**Agent**: `quant_researcher`Run FinBERT over the headlines to produce per-headline sentiment scores(positive/negative/neutral).Expected: `sentiment-scores`### Step 3: build-signal [depends_on: Step 2]**Agent**: `quant_researcher`Aggregate sentiment into a daily per-ticker signal; define the mapping fromsentiment to position (long/flat/short).Expected: `sentiment-signal`### Step 4: backtest [depends_on: Step 3]**Agent**: `quant_developer`Backtest the sentiment signal with realistic fees/slippage; audit for lookaheadbias. Report Sharpe, max drawdown, CAGR vs benchmark.Expected: `backtest-metrics`### Step 5: github-publish [depends_on: Step 4]**Agent**: `quant_developer`Publish a GitHub repo with README, metrics, clean code, and an honest "what didn'twork" section.Expected: `github-repo-url`### Step 6: kg-persist [depends_on: Step 5]**Agent**: `quant_researcher`**Tools**: `graph_write`Persist the project and metrics as typed nodes linked to the portfolio.## Output- A FinBERT-driven sentiment-alpha signal, backtested honestly- A published GitHub portfolio repo## 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 — scrape-news- **After level 0:** Step 2 — finbert-score- **After level 1:** Step 3 — build-signal- **After level 2:** Step 4 — backtest- **After level 3:** Step 5 — github-publish- **After level 4:** Step 6 — kg-persist**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