social-research-probe 0.0.3

Evidence-first social-media research CLI + Claude Code skill

social-research-probe

Evidence-first social-media research CLI + Claude Code skill

srp fetches YouTube content, scores it by trust/trend/opportunity, auto-corroborates claims, runs 15+ statistical models, and renders an HTML report — all from a single CLI command.

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Table of contents

1. What it does
2. Quickstart
3. Installation
4. Usage
5. Configuration
6. Documentation
7. Architecture
8. Contributing
9. Security
10. License
11. Changelog

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What it does

srp research runs a five-stage evidence pipeline:

1. Fetch — queries YouTube for recent content on a topic (configurable count and recency window).
2. Score — ranks items by a composite trust / trend / opportunity score derived from channel credibility, view velocity, and cross-channel repetition.
3. Enrich — fetches transcripts (yt-dlp captions → Whisper fallback) and generates 100-word LLM summaries for the top results.
4. Analyse — runs 15+ statistical models (regression, Bayesian linear, bootstrap, k-means, PCA, Kaplan–Meier, …) and renders charts as PNGs.
5. Synthesise — auto-corroborates the top results via Exa/Brave/Tavily, attaches structured LLM synthesis sections, and emits a Markdown + HTML report.

It also ships as a Claude Code skill (/srp) so you can invoke research, manage topics/purposes, and receive formatted reports directly inside a Claude Code session.

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Quickstart

pip install social-research-probe
srp config set-secret YOUTUBE_API_KEY
srp research "AI safety" "latest-news"

Or in natural-language mode (requires llm.runner configured):

srp research "what are researchers saying about model collapse?"

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Installation

# pip
pip install social-research-probe

# pipx (isolated environment, recommended for CLI tools)
pipx install social-research-probe

# uvx (run without installing)
uvx social-research-probe research "AI safety" "latest-news"

# From source (development)
git clone https://github.com/reshinto/social-research-probe
cd social-research-probe
pip install -e '.[dev]'

After installing, set your YouTube API key:

srp config set-secret YOUTUBE_API_KEY

See docs/installation.md for full setup including optional keys and the Claude Code skill bundle.

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Usage

# Research a topic with a specific purpose
srp research "AI agents" "latest-news"
srp research youtube "AI agents" "latest-news,trends"

# Natural-language query (auto-classifies topic + purpose)
srp research "who is winning the LLM benchmarks race?"

# Change how many videos are fetched (default: 20)
srp config set platforms.youtube.max_items 50

# Manage topics and purposes
srp update-topics --add "climate tech"
srp update-purposes --add "emerging-research"="Track peer-reviewed preprints"

# Config
srp config show
srp config set llm.runner claude
srp config set-secret YOUTUBE_API_KEY

# Install as a Claude Code skill
srp install-skill

Full flag reference: srp --help / srp <subcommand> --help.

See docs/usage.md for a complete guide including corroboration modes, report generation, and the pending-proposal workflow.

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Configuration

Key settings in ~/.social-research-probe/config.toml:

Setting	Default	What it controls
platforms.youtube.max_items	20	How many videos to fetch per search
platforms.youtube.recency_days	90	How far back to search (days)
llm.runner	none	Which LLM to use for summaries and synthesis
corroboration.backend	host	Which web-search backend to corroborate with

Change any setting with srp config set <key> <value>, e.g.:

srp config set platforms.youtube.max_items 50
srp config set platforms.youtube.recency_days 30
srp config set llm.runner claude

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Documentation

Document	What it covers
docs/README.md	Documentation hub — all docs by audience
docs/how-it-works.md	How fetching, scoring, and transcripts work; vs pure-LLM approach
docs/installation.md	Install (pip/pipx/uvx), API keys, LLM runner, verification
docs/usage.md	Run research, read output, configure video count
docs/corroboration.md	Claim corroboration: what it is, backends, configuration
docs/llm-runners.md	Supported runners, ensemble, what breaks without one
docs/statistics.md	All 15+ statistical models: what they measure, how to interpret
docs/charts.md	Every chart: what it shows, where it's saved, file persistence
docs/commands.md	Every flag, config key, exit code, environment variable
docs/architecture.md	System design, data flow, tradeoffs, known limitations
docs/adding-a-platform.md	How to add a new platform adapter (TikTok, Reddit, RSS)
docs/design-patterns.md	Patterns with why/why-not rationale
docs/testing.md	Test tiers, TDD workflow, 100% coverage gate
docs/security.md	Secret storage, network egress, hardening
CONTRIBUTING.md	Development workflow, TDD rules, file-size limits
SECURITY.md	Responsible disclosure policy
CHANGELOG.md	Release history

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Architecture

srp is a layered Python CLI. The CLI parses arguments and delegates to the research pipeline, which calls platform adapters, a scoring layer, a stats suite, an LLM ensemble, and corroboration backends, then feeds the result to the synthesis layer.

See docs/architecture.md for the full system design including data flow diagrams, async model, design tradeoffs, and extension points.

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Contributing

See CONTRIBUTING.md.

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Security

See SECURITY.md.

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License

MIT © 2026 Terence — see LICENSE.

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Changelog

See CHANGELOG.md.