gsd-framework-selector

gsd-framework-selector is an agent for Claude Code from megamen32/LastHumanCommit. It costs 54 tokens per session (1,605 once invoked), scanned A, original, MIT.

An interactive assistant that compares AI and large-language-model frameworks against a project's needs and returns a scored recommendation with reasons.

In plain words
What is it for?
Use it to select a framework for an AI integration or another AI system after reviewing the project's existing technology and asking about its use case.
Why use it?
Choosing an AI framework can involve trade-offs between the type of system, existing libraries, language, and team context. This makes those choices explicit before implementation.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool; mentions Codex; $skill-name invocation.

Part of the gsd plugin — 67 skills, 33 agents shipped together

Good fit Use it to select a framework for an AI integration or another AI system after reviewing the project's existing technology and asking about its use case.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/megamen32/lasthumancommit/gsd-framework-selector
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.

Clone the repo
git clone --depth 1 https://github.com/megamen32/LastHumanCommit

Made for: Claude Code.

Or install gsd, the plugin that ships this one along with the rest of its 67 skills, 33 agents.

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/megamen32/lasthumancommit/gsd-framework-selector/github.svg)](https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-framework-selector)
Your own site
<a href="https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-framework-selector"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-framework-selector/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for gsd-framework-selector

Your own site · 80×15
<a href="https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-framework-selector"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-framework-selector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,605 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00054 $0.01605
Opus 5 $0.00027 $0.00803
Sonnet 5 $0.00011 $0.00321
Haiku 4.5 $0.00005 $0.00161

Measured today against content hash 5f38259c3455, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 today.

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.

plugins/gsd/agents/gsd-framework-selector.md · 166 lines

How it starts

The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<codex_agent_role> role: gsd-framework-selector tools: Read, Bash, Grep, Glob, WebSearch, AskUserQuestion purpose: Presents an interactive decision matrix to surface the right AI/LLM framework for the user's specific use case. Produces a scored recommendation with rationale. Spawned by $gsd-ai-integration-phase and $gsd-select-framework orchestrators. </codex_agent_role>

<required_reading> Read /tmp/gsd-npm-codex-stage/get-shit-done/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 (the agent)", 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 · 166 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. today First seen · 166 lines · 54 tokens per session scan A 5f38259c3455

Subscribe to this mod's changes

gsd-framework-selector is an agent published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 1,605 once invoked, about $0.0003 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 2026-09-12.

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