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.
npx agentmods add agents/mrboups/xbrain/gsd-framework-selectorgit clone --depth 1 https://github.com/mrboups/xbrainWhat 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 | $0.00054 | $0.01539 |
| Opus 5 | $0.00027 | $0.00770 |
| Sonnet 5 | $0.00011 | $0.00308 |
| Haiku 4.5 | $0.00005 | $0.00154 |
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 yesterday.
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.
This is a copy
97% identical to gsd-framework-selector — 4 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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<required_reading>
Read D:/VSC/xbrain/.claude/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 (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" }
]
}
])
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.
- yesterday First seen · 161 lines · 54 tokens per session scan A 54523f801b03
gsd-framework-selector is an agent published in the GitHub repository mrboups/xbrain (2 stars, last pushed 18d ago), licensed MIT. It adds 54 tokens to every session and 1,539 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to gsd-framework-selector, differing in 4 lines, and is treated as a copy.
Other agents, from other repositories
slm-loop-runner
Runs a task as a bounded loop backed by SuperLocalMemory: iterate until an INDEPENDENT gate passes — never the agent's own claim. Delegate here when a task has a checkable acceptance condition (a test suite, a JSON schema, a linter, a reconciliation rule, a security scan) and you want gate-verified completion with an…
context-researcher
On-demand research agent that decomposes queries into multiple search angles, runs parallel memory lookups, and synthesizes a structured briefing. Use when deep memory context is needed for a topic, entity, or decision.
orchestrator
Role: Coordinate planner and workers in multi-agent PDF pipeline Workflow: pdf-pipeline Namespace: team:eng.
planner
Role: Route documents to the correct processing pipeline Workflow: pdf-pipeline Step: planner Namespace: team:eng.
vkm-implementer
Terse minimal-diff executor for well-specified implementation tasks. Give it a precise spec (ideally from /vkm-spec) and the target files; it implements with dense code, runs the checks, and reports only the decisive evidence.
Demonstrate
Agent for demonstrating VS Code features.