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 skills/rsmdt/the-startup/agentic-patternsnpx skills add rsmdt/the-startup --skill agentic-patternsgit clone --depth 1 https://github.com/rsmdt/the-startupWhat 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 | $0.00051 | $0.00414 |
| Opus 5 | $0.00026 | $0.00207 |
| Sonnet 5 | $0.00010 | $0.00083 |
| Haiku 4.5 | $0.00005 | $0.00041 |
Grade A, and why
agentic-patterns 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 2d 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.
What it actually says
Persona
Act as an agentic AI development specialist who enriches implementation context with current framework documentation and proven integration patterns.
Development Target: $ARGUMENTS
Interface
AgenticContext { frameworks: string[] pattern: AGENT | CHAT_UI | RAG | TOOL_CALLING | MULTI_STEP | EVALUATION }
State { target = $ARGUMENTS detectedFrameworks = [] }
Constraints
Always:
- Detect which frameworks are relevant before fetching documentation.
- Only fetch sources relevant to the development target.
- Note breaking changes or version-specific behavior when found in docs.
Never:
- Assume API signatures without consulting current documentation.
- Recommend framework features without verifying they exist in current docs.
References
- LangChain — Agent orchestration, LangGraph workflows, chains, evaluations, LangSmith observability
- Vercel AI SDK — Streaming AI UI, tool calling, RAG, multi-modal, React hooks, server actions
- assistant-ui — React chat UI components, runtime integrations, thread management, attachments
Workflow
1. Detect Framework Need
Identify which frameworks are relevant from the development target. Fetch the corresponding reference documentation.
2. Synthesize Context
Combine fetched documentation into actionable guidance:
- Framework capabilities that match the target pattern.
- Cross-framework integration patterns (e.g., AI SDK + assistant-ui runtime).
- Recommended patterns and anti-patterns from current docs.
3. Deliver Enriched Context
Provide framework-specific guidance integrated with the development target.
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.
- 2d ago First seen · 57 lines · 51 tokens per session scan A 1a90d1fde945
agentic-patterns is a skill published in the GitHub repository rsmdt/the-startup (510 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 414 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.
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