How do I install the Sentiment News skill?

Run npx skillmds@latest add kuntal-r-d/sentiment-news-2 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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Is Sentiment News free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Sentiment News?

kuntal-r-d (@kuntal-r-d) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Sentiment News by kuntal-r-d — Agent Skill

Name: Sentiment News
Author: kuntal-r-d

Sentiment News

Computes ticker/sector news sentiment for DSE stocks and separates rumour-driven items from fundamentals-driven news, since the Dhaka Stock Exchange is rumour-heavy. Use when the user asks about news sentiment, "what's the news saying about GP", headline tone, rumour vs real news, or wants a sentiment score to feed signal-synthesizer.

kuntal-r-d Updated Sep 17, 2026

File contents

Sentiment & News

When to use

Activate when a user wants the news read on a DSE stock or sector: "what's the sentiment on GP?", "is the news good or bad?", "is this just a rumour?", "score the headlines". This is the Sentiment Agent (PRD-002 REQ-019). Its score is designed to feed signal-synthesizer alongside the technical and fundamental legs.

What it does

Reads the shared contract's news array and produces an aggregate sentiment score in [-1, +1]. Because the DSE is rumour-heavy (PRD-001 REQ-023), it explicitly separates rumour-driven from fundamentals-driven items:

Lexicon scoring — each headline is scored from positive vs negative keyword counts (stdlib only; no NLP libraries).
Rumour detection — an item is a RUMOUR if its source is social/forum/ unconfirmed/rumour, its category is rumour-like, or its headline mentions rumour/unconfirmed/speculation. Rumours are down-weighted to x0.3 and, when they are the only material signal, the score is capped to [-0.4, +0.4] so a rumour alone can never drive a strong signal.
Fundamental classification — items in earnings, regulatory, corporate_action, or macro carry full weight.

Output is a Thinking Card (see suite README).

How to run

python3 scripts/sentiment.py --input data.json --pretty
cat data.json | python3 scripts/sentiment.py

Reads news (required) and optional mode/ticker/as_of. Returns score (-1..+1, fundamental-weighted), confidence, rating (positive/neutral/negative), key_metrics (item/fundamental/rumour counts, average fundamental sentiment), per-item reasoning, and flags.

Try it now with the shared fixture:

python3 scripts/sentiment.py --input ../_fixtures/sample_input.json --pretty

Interpreting output

rating: positive (score >=0.2), negative (<=-0.2), else neutral.
flags: rumour_dominated (no fundamental items, score capped) and thin_coverage (<2 items) both lower confidence — never silent drops.
Compare avg_fundamental_sentiment against the headline score to see how much of the read is fundamentals vs noise.

Notes

The lexicon, rumour-vs-news separation rationale, and DSE rumour context are documented in references/SENTIMENT.md. Output is educational analysis only, never financial advice.

---name: sentiment-news-2description: Computes ticker/sector news sentiment for DSE stocks and separates rumour-driven items from fundamentals-driven news, since the Dhaka Stock Exchange is rumour-heavy. Use when the user asks about news sentiment, "what's the news saying about GP", headline tone, rumour vs real news, or wants a sentiment score to feed signal-synthesizer.license: Apache-2.0---# Sentiment & News## When to useActivate when a user wants the news read on a DSE stock or sector: "what's thesentiment on GP?", "is the news good or bad?", "is this just a rumour?", "score theheadlines". This is the Sentiment Agent (PRD-002 REQ-019). Its score is designed tofeed `signal-synthesizer` alongside the technical and fundamental legs.## What it doesReads the shared contract's `news` array and produces an aggregate sentiment score in`[-1, +1]`. Because the DSE is rumour-heavy (PRD-001 REQ-023), it explicitly**separates rumour-driven from fundamentals-driven items**:- **Lexicon scoring** — each headline is scored from positive vs negative keyword counts (stdlib only; no NLP libraries).- **Rumour detection** — an item is a `RUMOUR` if its `source` is social/forum/ unconfirmed/rumour, its category is rumour-like, or its headline mentions rumour/unconfirmed/speculation. Rumours are down-weighted to **x0.3** and, when they are the *only* material signal, the score is capped to `[-0.4, +0.4]` so a rumour alone can never drive a strong signal.- **Fundamental classification** — items in `earnings`, `regulatory`, `corporate_action`, or `macro` carry full weight.Output is a Thinking Card (see suite README).## How to run```bashpython3 scripts/sentiment.py --input data.json --prettycat data.json | python3 scripts/sentiment.py```Reads `news` (required) and optional `mode`/`ticker`/`as_of`. Returns `score`(-1..+1, fundamental-weighted), `confidence`, `rating` (positive/neutral/negative),`key_metrics` (item/fundamental/rumour counts, average fundamental sentiment),per-item `reasoning`, and `flags`.Try it now with the shared fixture:```bashpython3 scripts/sentiment.py --input ../_fixtures/sample_input.json --pretty```## Interpreting output- `rating`: `positive` (score >=0.2), `negative` (<=-0.2), else `neutral`.- `flags`: `rumour_dominated` (no fundamental items, score capped) and `thin_coverage` (<2 items) both lower confidence — never silent drops.- Compare `avg_fundamental_sentiment` against the headline `score` to see how much of the read is fundamentals vs noise.## NotesThe lexicon, rumour-vs-news separation rationale, and DSE rumour context aredocumented in [references/SENTIMENT.md](references/SENTIMENT.md). Output iseducational analysis only, never financial advice.

Frequently asked questions