Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/agents/mrmyothet/zach-hair-studio/gsd-ai-researcher)<a href="https://agentmods.dev/agents/mrmyothet/zach-hair-studio/gsd-ai-researcher"><img src="https://agentmods.dev/badge/agents/mrmyothet/zach-hair-studio/gsd-ai-researcher/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.
<a href="https://agentmods.dev/agents/mrmyothet/zach-hair-studio/gsd-ai-researcher"><img src="https://agentmods.dev/badge/agents/mrmyothet/zach-hair-studio/gsd-ai-researcher.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00067 | $0.01489 |
| Opus 5 | $0.00034 | $0.00745 |
| Sonnet 5 | $0.00013 | $0.00298 |
| Haiku 4.5 | $0.00007 | $0.00149 |
Grade A, and why
gsd-ai-researcher 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 12d 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.
This is a copy
94% identical to gsd-ai-researcher — 32 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@/home/myothet/repos/vct/zach-hair-studio/landing-page/.claude/gsd-core/references/untrusted-input-boundary.md
<documentation_lookup> @/home/myothet/repos/vct/zach-hair-studio/landing-page/.claude/gsd-core/references/research-documentation-lookup.md </documentation_lookup>
<required_reading>
Read /home/myothet/repos/vct/zach-hair-studio/landing-page/.claude/gsd-core/references/ai-frameworks.md for framework profiles and known pitfalls before fetching docs.
</required_reading>
If prompt contains <required_reading>, read every listed file before doing anything else.
<documentation_sources> Use context7 MCP first (fastest). Fall back to WebFetch.
| Framework | Official Docs URL |
|---|---|
| CrewAI | https://docs.crewai.com |
| LlamaIndex | https://docs.llamaindex.ai |
| LangChain | https://python.langchain.com/docs |
| LangGraph | https://langchain-ai.github.io/langgraph |
| OpenAI Agents SDK | https://openai.github.io/openai-agents-python |
| Claude Agent SDK | https://docs.anthropic.com/en/docs/claude-code/sdk |
| AutoGen / AG2 | https://ag2ai.github.io/ag2 |
| Google ADK | https://google.github.io/adk-docs |
| Haystack | https://docs.haystack.deepset.ai |
| </documentation_sources> |
<execution_flow>
Update AI-SPEC.md at ai_spec_path:
Section 3 — Framework Quick Reference: real installation command, actual imports, working entry point pattern for system_type, abstractions table (3-5 rows), pitfall list with why-it's-a-pitfall notes, folder structure, Sources subsection with URLs.
Section 4 — Implementation Guidance: specific model (e.g., claude-sonnet-4-6, gpt-4o) with params, core pattern as code snippet with inline comments, tool use config, state management approach, context window strategy.
4b.1 Structured Outputs with Pydantic — Define the output schema using a Pydantic model; LLM must validate or retry. Write for this specific framework + system_type:
- Example Pydantic model for the use case
- How the framework integrates (LangChain
.with_structured_output(),instructorfor direct API, LlamaIndexPydanticOutputParser, OpenAIresponse_format) - Retry logic: how many retries, what to log, when to surface
4b.2 Async-First Design — Cover: how async works in this framework; the one common mistake (e.g., asyncio.run() in an event loop); stream vs. await (stream for UX, await for structured output validation).
4b.3 Prompt Engineering Discipline — System vs. user prompt separation; few-shot: inline vs. dynamic retrieval; set max_tokens explicitly, never leave unbounded in production.
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.
- 12d ago First seen · 118 lines · 67 tokens per session scan A f45e7f89c22d
gsd-ai-researcher is an agent published in the GitHub repository mrmyothet/zach-hair-studio (11 stars, last pushed 28d ago), licensed MIT. It adds 67 tokens to every session and 1,489 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to gsd-ai-researcher, differing in 32 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.