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.
git clone --depth 1 https://github.com/gsd-build/get-shit-doneWrote 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/gsd-build/get-shit-done/gsd-ai-researcher)<a href="https://agentmods.dev/agents/gsd-build/get-shit-done/gsd-ai-researcher"><img src="https://agentmods.dev/badge/agents/gsd-build/get-shit-done/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/gsd-build/get-shit-done/gsd-ai-researcher"><img src="https://agentmods.dev/badge/agents/gsd-build/get-shit-done/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 | $0.00068 | $0.01624 |
| Opus 5 | $0.00034 | $0.00812 |
| Sonnet 5 | $0.00014 | $0.00325 |
| Haiku 4.5 | $0.00007 | $0.00162 |
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 11d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- gsd-ai-researcher — 100% identical, 4 lines differ
- gsd-ai-researcher — 100% identical, 2 lines differ
- gsd-ai-researcher — 100% identical, 4 lines differ
- gsd-ai-researcher — 98% identical, 36 lines differ
- gsd-ai-researcher — 97% identical, 11 lines differ
- gsd-ai-researcher — 94% identical, 31 lines differ
- gsd-ai-researcher — 94% identical, 32 lines differ
- ferrox-ai-researcher — 94% identical, 37 lines differ
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<documentation_lookup> When you need library or framework documentation, check in this order:
-
If Context7 MCP tools (
mcp__context7__*) are available in your environment, use them:- Resolve library ID:
mcp__context7__resolve-library-idwithlibraryName - Fetch docs:
mcp__context7__get-library-docswithcontext7CompatibleLibraryIdandtopic
- Resolve library ID:
-
If Context7 MCP is not available (upstream bug anthropics/claude-code#13898 strips MCP tools from agents with a
tools:frontmatter restriction), use the CLI fallback via Bash:Step 1 — Resolve library ID:
npx --yes ctx7@latest library <name> "<query>"Step 2 — Fetch documentation:
npx --yes ctx7@latest docs <libraryId> "<query>"
Do not skip documentation lookups because MCP tools are unavailable — the CLI fallback works via Bash and produces equivalent output. </documentation_lookup>
<required_reading>
Read ~/.claude/get-shit-done/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
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.
- 11d ago First seen · 134 lines · 68 tokens per session scan A 783db224008e
gsd-ai-researcher is an agent published in the GitHub repository gsd-build/get-shit-done (64,561 stars, last pushed 3mo ago), licensed MIT. It adds 68 tokens to every session and 1,624 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 2026-08-30.
Other agents, from other repositories
gsd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gsd:eval-review orchestrator.
gsd-domain-researcher
Researches the business domain and real-world application context of the AI system being built. Surfaces domain expert evaluation criteria, industry-specific failure modes, regulatory context, and what "good" looks like for practitioners in this field — before the eval-planner turns it into measurable rubrics. Spawned…
gsd-framework-selector
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.
gsd-ai-researcher
Researches a chosen AI framework's official docs to produce implementation-ready guidance — best practices, syntax, core patterns, and pitfalls distilled for the specific use case. Writes the Framework Quick Reference and Implementation Guidance sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.
gsd-eval-planner
Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase…
gsd-executor
Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.