gsd-framework-selector

gsd-framework-selector is an agent for coding agents from open-gsd/gsd-core. It costs 55 tokens per session (1,513 once invoked), scanned A, a copy of gsd-framework-selector, MIT.

An interactive guide for choosing an AI or large-language-model framework for a specific project. It first checks the project’s existing technology choices and then compares options.

In plain words
What is it for?
Selecting frameworks for document question-answering, multi-agent workflows, chatbots, and structured data extraction.
Why use it?
It reduces the risk of choosing a framework that does not fit the system type, team, or tools already in use.

Agent

Part of the gsd-core plugin — 18 skills, 38 agents, 7 hooks shipped together

Install

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.

agentmods
npx agentmods add agents/open-gsd/gsd-core/gsd-framework-selector
Clone the repo
git clone --depth 1 https://github.com/open-gsd/gsd-core

Or install gsd-core, the plugin that ships this one along with the rest of its 18 skills, 38 agents, 7 hooks.

Wrote 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.

agentmods badge for gsd-framework-selector

README.md
[![agentmods](https://agentmods.dev/badge/agents/open-gsd/gsd-core/gsd-framework-selector.svg)](https://agentmods.dev/agents/open-gsd/gsd-core/gsd-framework-selector)
Your own site
<a href="https://agentmods.dev/agents/open-gsd/gsd-core/gsd-framework-selector"><img src="https://agentmods.dev/badge/agents/open-gsd/gsd-core/gsd-framework-selector.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,513 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.01513
Opus 5 $0.00028 $0.00757
Sonnet 5 $0.00011 $0.00303
Haiku 4.5 $0.00006 $0.00151

Measured 4d ago against content hash ab16fc97eb92, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

gsd-framework-selector 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.

Origin

This is a copy

91% identical to gsd-framework-selector — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agents/gsd-framework-selector.md · 159 lines

How it starts

The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<required_reading> Read ~/.claude/gsd-core/references/ai-frameworks.md before asking questions. This is your decision matrix. </required_reading>

<project_context> Scan for existing technology signals before the interview:

find . -maxdepth 2 \( -name "package.json" -o -name "pyproject.toml" -o -name "requirements*.txt" \) -not -path "*/node_modules/*" 2>/dev/null | head -5

Read found files to extract: existing AI libraries, model providers, language, team size signals. This prevents recommending a framework the team has already rejected. </project_context>

AskUserQuestion([
  {
    question: "What type of AI system are you building?",
    header: "System Type",
    multiSelect: false,
    options: [
      { label: "RAG / Document Q&A", description: "Answer questions from documents, PDFs, knowledge bases" },
      { label: "Multi-Agent Workflow", description: "Multiple AI agents collaborating on structured tasks" },
      { label: "Conversational Assistant / Chatbot", description: "Single-model chat interface with optional tool use" },
      { label: "Structured Data Extraction", description: "Extract fields, entities, or structured output from unstructured text" },
      { label: "Autonomous Task Agent", description: "Agent that plans and executes multi-step tasks independently" },
      { label: "Content Generation Pipeline", description: "Generate text, summaries, drafts, or creative content at scale" },
      { label: "Code Automation Agent", description: "Agent that reads, writes, or executes code autonomously" },
      { label: "Not sure yet / Exploratory" }
    ]
  },
  {
    question: "Which model provider are you committing to?",
    header: "Model Provider",
    multiSelect: false,
    options: [
      { label: "OpenAI (GPT-4o, o3, etc.)", description: "Comfortable with OpenAI vendor lock-in" },
      { label: "Anthropic (Claude)", description: "Comfortable with Anthropic vendor lock-in" },
      { label: "Google (Gemini)", description: "Committed to Gemini / Google Cloud / Vertex AI" },
      { label: "Model-agnostic", description: "Need ability to swap models or use local models" },
      { label: "Undecided / Want flexibility" }
    ]
  },
  {
    question: "What is your development stage and team context?",
    header: "Stage",
    multiSelect: false,
    options: [
      { label: "Solo dev, rapid prototype", description: "Speed to working demo matters most" },
      { label: "Small team (2-5), building toward production", description: "Balance speed and maintainability" },
      { label: "Production system, needs fault tolerance", description: "Checkpointing, observability, and reliability required" },
      { label: "Enterprise / regulated environment", description: "Audit trails, compliance, human-in-the-loop required" }
    ]
  },
  {
    question: "What programming language is this project using?",
    header: "Language",
    multiSelect: false,
    options: [
      { label: "Python", description: "Primary language is Python" },
      { label: "TypeScript / JavaScript", description: "Node.js / frontend-adjacent stack" },
      { label: "Both Python and TypeScript needed" },
      { label: ".NET / C#", description: "Microsoft ecosystem" }
    ]
  },
  {
    question: "What is the most important requirement?",
    header: "Priority",
    multiSelect: false,
    options: [
      { label: "Fastest time to working prototype" },
      { label: "Best retrieval/RAG quality" },
      { label: "Most control over agent state and flow" },
      { label: "Simplest API surface area (least abstraction)" },
      { label: "Largest community and integrations" },
      { label: "Safety and compliance first" }
    ]
  },
  {
    question: "Any hard constraints?",
    header: "Constraints",
    multiSelect: true,
    options: [
      { label: "No vendor lock-in" },
      { label: "Must be open-source licensed" },
      { label: "TypeScript required (no Python)" },
      { label: "Must support local/self-hosted models" },
      { label: "Enterprise SLA / support required" },
      { label: "No new infrastructure (use existing DB)" },
      { label: "None of the above" }
    ]
  }
])

Read the full file on GitHub · 159 lines

Changes

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.

  1. 4d ago First seen · 159 lines · 55 tokens per session scan A ab16fc97eb92

Subscribe to this mod's changes

gsd-framework-selector is an agent published in the GitHub repository open-gsd/gsd-core (9,086 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,513 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to gsd-framework-selector, differing in 8 lines, and is treated as a copy.

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