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 skills add juspay/neurolink --skill neurolink-guidegit clone --depth 1 https://github.com/juspay/neurolinkWrote 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/skills/juspay/neurolink/neurolink-guide)<a href="https://agentmods.dev/skills/juspay/neurolink/neurolink-guide"><img src="https://agentmods.dev/badge/skills/juspay/neurolink/neurolink-guide/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/skills/juspay/neurolink/neurolink-guide"><img src="https://agentmods.dev/badge/skills/juspay/neurolink/neurolink-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 130 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00065 | $0.01233 |
| Opus 5.5 | $0.00026 | $0.00493 |
| Sonnet 5.5 | $0.00013 | $0.00247 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
neurolink-guide 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 26d 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeuroLink Usage Guide
NeuroLink is an enterprise AI development platform providing unified access to supported AI providers (text, decision-making, voice, multimodal) through a single API. It ships as both a TypeScript SDK (@juspay/neurolink) and a professional CLI.
Quick Navigation
Based on your query, I'll guide you to the right documentation:
- Getting Started → Read sdk-quickstart.md
- Provider Setup → Read providers.md
- Multimodal (images, PDFs, files) → Read multimodal.md
- MCP Tools Integration → Read tools-mcp.md
- RAG Pipelines → Read rag-integration.md
- Conversation Memory → Read memory-conversations.md
- CLI Commands → Read cli-reference.md
- Advanced Features → Read advanced-features.md
- Troubleshooting → Read troubleshooting.md
Topic Routing
If the user asked about $ARGUMENTS:
| Topic Keywords | Reference File |
|---|---|
| install, setup, start, begin, quickstart | sdk-quickstart.md |
| provider, openai, anthropic, vertex, bedrock, azure, gemini, claude, model | providers.md |
| image, pdf, csv, excel, document, file, multimodal, vision | multimodal.md |
| tool, mcp, server, GitHub, external, function | tools-mcp.md |
| rag, retrieval, chunk, vector, embed, document search | rag-integration.md |
| memory, conversation, history, session, redis, context | memory-conversations.md |
| cli, command, terminal, generate, stream, loop, serve | cli-reference.md |
| hitl, workflow, agent, observe, telemetry, deploy, server | advanced-features.md |
| error, issue, problem, fix, debug, not working | troubleshooting.md |
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- advanced-features.md 13 KB
- cli-reference.md 9.3 KB
- memory-conversations.md 6.1 KB
- multimodal.md 8.2 KB
- providers.md 9.8 KB
- rag-integration.md 10 KB
- sdk-quickstart.md 6.8 KB
- templates/basic-setup.ts 2.7 KB runs code
- templates/rag-pipeline.ts 6.7 KB runs code
- templates/server-deploy.ts 9.0 KB runs code
- templates/streaming.ts 4.7 KB runs code
- templates/with-tools.ts 7.5 KB runs code
- tools-mcp.md 8.8 KB
- troubleshooting.md 9.1 KB
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.
- 26d ago Changed 62c5d18965be
- 1mo ago Changed 03196d204c05
- 1mo ago First seen · 164 lines · 65 tokens per session scan A bd2f97c0ba89
neurolink-guide is a skill published in the GitHub repository juspay/neurolink (157 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 1,233 once invoked, about $0.0003 per session on Opus 5.5. 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 skills, from other repositories
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
ai-engineer
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design…
pn-rag-evaluation
Evaluate retrieval-augmented generation pipelines — golden sets, automated metrics (incl. RAGAS-style), human rubrics, regression gates in CI. Use when building or changing RAG (chunks, embeddings, rerankers, prompts) and need quality proof, not vibes.
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
install-axon
Use when installing or repairing Axon, choosing Incus or systemd, configuring bearer/OAuth auth, exposing MCP, selecting Codex app-server synthesis, connecting an agent, or verifying the RAG stack.