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Agent skill

Algo Social Sentiment

by asgard-ai-platform in asgard-ai-platform/skills

Implement VADER sentiment analysis for social media text scoring.

MITAuto-check passedWriting & Content

Install Algo Social Sentiment

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-social-sentiment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-social-sentiment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-social-sentiment .claude/skills/algo-social-sentiment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
algo-social-sentiment
GitHub stars
242
Token cost
~995 tokens
SKILL.md length
373 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Implement VADER sentiment analysis for social media text scoring.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to analyze sentiment in tweets
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Social Sentiment is an agent skill from asgard-ai-platform/skills. Implement VADER sentiment analysis for social media text scoring. Use this skill when the user needs to analyze sentiment in tweets, reviews, or social posts, compute compound sentiment scores, or classify text polarity — even if they say 'is this positive or negative', 'sentiment of these comments', or 'social media mood analysis'.

Its SKILL.md is about 1000 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/model-comparison.md` and `references/vader-rules.md`).

It sits in Writing & Content, covering Social media posts and Customer feedback analysis. It works with X (Twitter). The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs to analyze sentiment in tweets
  • Compute compound sentiment scores
  • Classify text polarity — even if they say is this positive
  • Sentiment of these comments

Example prompts

  • “is this positive or negative”
  • “sentiment of these comments”
  • “social media mood analysis”
  • “/algo-social-sentiment”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Algo Social Sentiment loads about 995 tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 373 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~995
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 373 words, ~995 tokens.

Download SKILL.mdSave it as .claude/skills/algo-social-sentiment/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-social-sentiment
description
Implement VADER sentiment analysis for social media text scoring. Use this skill when the user needs to analyze sentiment in tweets, reviews, or social posts, compute compound sentiment scores, or classify text polarity — even if they say 'is this positive or negative', 'sentiment of these comments', or 'social media mood analysis'.
metadata.category
WP-38 社群演算法
metadata.tags
social-media, sentiment-analysis, vader, nlp

VADER Sentiment Analysis

Overview

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment tool optimized for social media. Returns compound score [-1, +1] combining positive, negative, and neutral proportions. Runs in O(n) per text where n = word count. No training required.

When to Use

Trigger conditions:

  • Analyzing sentiment in social media posts, tweets, or reviews
  • Quick sentiment scoring without ML model training
  • Processing text with slang, emoticons, and informal language

When NOT to use:

  • For formal/academic text (VADER is tuned for social media)
  • When domain-specific sentiment matters (e.g., financial sentiment — use FinBERT)
  • When sarcasm detection is critical (VADER doesn't detect sarcasm)

Algorithm

IRON LAW: VADER Is Designed for SOCIAL MEDIA Text
It handles slang, emoticons, capitalization, and punctuation as
sentiment modifiers. Applying it to formal documents (legal, academic,
medical) produces unreliable scores. For domain-specific text, use
domain-trained models instead.
Phase 1: Input Validation

Tokenize text. Preserve: capitalization (ALL CAPS = emphasis), punctuation (! amplifies), emoticons/emoji. Gate: Text is non-empty, encoding handled correctly.

Phase 2: Core Algorithm
  1. Look up each token in VADER lexicon (7,500+ sentiment-rated terms)
  2. Apply grammatical rules: negation ("not good" = negative), degree modifiers ("very good" > "good"), capitalization boost, punctuation amplification
  3. Compute raw valence scores for positive, negative, neutral proportions
  4. Compute compound score: normalized sum of all valence scores using formula: compound = sum / √(sum² + α) where α = 15
Phase 3: Verification

Classify: compound ≥ 0.05 → positive, ≤ -0.05 → negative, else neutral. Spot-check sample results. Gate: Classifications pass manual spot-check on 10-20 examples.

Phase 4: Output

Return compound score and polarity classification per text.

Output Format

json
{
  "results": [{"text": "...", "compound": 0.76, "pos": 0.45, "neu": 0.55, "neg": 0.0, "label": "positive"}],
  "metadata": {"texts_analyzed": 500, "distribution": {"positive": 0.45, "neutral": 0.35, "negative": 0.20}}
}

Examples

Show full SKILL.md (149 more words)Show less
Sample I/O

Input: "This product is AMAZING!!! 😍" Expected: compound ≈ 0.87 (positive). Boosted by: CAPS, !!!, 😍 emoji.

Edge Cases
InputExpectedWhy
"Not bad at all"Slightly positive (~0.2)Double negation partially handled
"😂😂😂"PositiveEmoji mapped in lexicon
Empty stringCompound = 0, neutralNo tokens to score

Gotchas

  • Sarcasm is invisible: "Oh great, another meeting" reads as positive. VADER has no sarcasm detection.
  • Negation window: VADER applies negation within a 3-word window. "I do not think this is bad" may misparse the negation chain.
  • Emoji coverage: VADER's emoji lexicon may not cover newer emoji. Update or supplement as needed.
  • Language limitation: VADER is English-only. For Chinese/Japanese, use language-specific tools (e.g., SnowNLP for Chinese).
  • Compound threshold sensitivity: The 0.05 boundary is arbitrary. Adjust thresholds based on your specific use case and tolerance for false positives.

References

  • For VADER lexicon and rules documentation, see references/vader-rules.md
  • For comparison with transformer-based sentiment models, see references/model-comparison.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in algo-social-sentiment of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/model-comparison.md
  • references/vader-rules.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Social Sentiment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Algo Social Sentiment compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Algo Social Sentiment this skillasgard-ai-platform/skills242—~995Automated safety check: PassMIT
Twitter APIaiskillstore/marketplace433—~2.2kAutomated safety check: PassNone
Content Analysisliangdabiao/claude-data-analysis-ultra-main290—~1.7kAutomated safety check: NotesNone
Twitter Searchsundial-org/awesome-openclaw-skills663—~2.3kAutomated safety check: PassNone
Socialcoreyhaines31/marketingskills54k4 repos~4.5kAutomated safety check: PassMIT
Social Contentfreekmurze/dotfiles1k23 repos~2.1kAutomated safety check: PassNone

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Works with

Categories

Questions about Algo Social Sentiment

What does Algo Social Sentiment do?

Implement VADER sentiment analysis for social media text scoring. Algo Social Sentiment is an agent skill from asgard-ai-platform/skills. Implement VADER sentiment analysis for social media text scoring.

When should I use Algo Social Sentiment?

Algo Social Sentiment fits situations like: the user needs to analyze sentiment in tweets; compute compound sentiment scores; classify text polarity — even if they say is this positive; sentiment of these comments.

How do I install Algo Social Sentiment in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-social-sentiment -a claude-code`. Or copy the skill folder (algo-social-sentiment in asgard-ai-platform/skills) into .claude/skills/algo-social-sentiment in your project. Claude Code loads it when a task matches its description.

How do I install Algo Social Sentiment in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-social-sentiment -a codex`. Or copy the skill folder (algo-social-sentiment in asgard-ai-platform/skills) into .agents/skills/algo-social-sentiment in your project. Codex loads it when a task matches its description.

Can I use Algo Social Sentiment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-social-sentiment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-social-sentiment, .gemini/skills/algo-social-sentiment, .github/skills/algo-social-sentiment and .opencode/skills/algo-social-sentiment in your project.

What does Algo Social Sentiment need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Social Sentiment is instructions for the agent only.

Does Algo Social Sentiment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Algo Social Sentiment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Algo Social Sentiment use?

Algo Social Sentiment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo Social Sentiment use?

About 995 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.1k tokens, read only when the agent opens those files.

What are the alternatives to Algo Social Sentiment?

Skills that share tags, products or a category with Algo Social Sentiment: Twitter API (aiskillstore/marketplace, 433 stars), Content Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars), Twitter Search (sundial-org/awesome-openclaw-skills, 663 stars) and Social (coreyhaines31/marketingskills, 54k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Social Sentiment?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.