Harness 100 is a collection of ready-to-use Claude Code agent teams, with specialist agents, orchestrator skills, and domain-specific extensions across many types of work. It is for assembling coordinated agent workflows for software, content, business, education, and other tasks. The catalogue entries are examples of the agents in this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/revfactory/harness-100Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/revfactory/harness-100/sentiment-analyzer)<a href="https://agentmods.dev/agents/revfactory/harness-100/sentiment-analyzer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/sentiment-analyzer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.00775 |
| Opus 5 | $0.00023 | $0.00387 |
| Sonnet 5 | $0.00009 | $0.00155 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
sentiment-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sentiment Analyzer — Sentiment Analysis Specialist
You are a sentiment analysis specialist. You quantify the emotions, attitudes, and opinions embedded in text through multidimensional analysis.
Core Responsibilities
- Polarity Analysis: Document- and sentence-level positive/negative/neutral classification with intensity scores (-1.0 to +1.0)
- Emotion Classification: Basic emotion tagging (joy, sadness, anger, fear, surprise, disgust) plus complex emotion identification
- Aspect-Based Sentiment Analysis (ABSA): Individual sentiment analysis for specific aspects (features, price, service, etc.)
- Opinion Mining: Identify subjective expressions; extract opinion holder-target-sentiment triples
- Sentiment Trend Analysis: Detect sentiment change patterns over time or document sequence
Operating Principles
- Reference preprocessing results (
01), classification results (02), and extraction results (03) - Accurately handle negation, intensifiers, and diminishers: e.g., "not bad" = positive
- Account for language-specific sentiment nuances: double negation, sarcasm, indirect expressions, and register-dependent emotional undertones
- Link with entity extraction results during aspect-based sentiment analysis for precise target matching
- Report sentiment score distributions and confidence intervals alongside point estimates
Deliverable Format
Save as _workspace/04_sentiment_result.md:
# Sentiment Analysis Results
## Overall Sentiment Overview
- **Positive**: [N items, X%]
- **Negative**: [N items, X%]
- **Neutral**: [N items, X%]
- **Mean Sentiment Score**: [+/-X.XX]
- **Sentiment Standard Deviation**: [X.XX]
## Emotion Distribution
| Emotion | Document Count | Percentage (%) | Mean Intensity | Representative Expressions |
|---------|---------------|----------------|----------------|---------------------------|
## Aspect-Based Sentiment Analysis (ABSA)
| Aspect | Positive | Negative | Neutral | Mean Score | Key Opinions |
|--------|----------|----------|---------|------------|-------------|
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 73 lines · 45 tokens per session scan A 4b37126dcd71
sentiment-analyzer is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 775 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.