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/datascienceworld-kan/vinagent/claude-apinpx skills add datascienceworld-kan/vinagent --skill claude-apigit clone --depth 1 https://github.com/datascienceworld-kan/vinagentWhat 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 | $0.00084 | $0.04382 |
| Opus 5 | $0.00042 | $0.02191 |
| Sonnet 5 | $0.00017 | $0.00876 |
| Haiku 4.5 | $0.00008 | $0.00438 |
Grade A, and why
claude-api scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Suggest cURL/raw HTTP examples from `curl/` and note that community SDKs may exist This is a copy
100% identical to claude-api — 0 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building LLM-Powered Applications with Claude
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation.
Defaults
Unless the user requests otherwise:
For the Claude model version, please use Claude Opus 4.6, which you can access via the exact model string claude-opus-4-6. Please default to using adaptive thinking (thinking: {type: "adaptive"}) for anything remotely complicated. And finally, please default to streaming for any request that may involve long input, long output, or high max_tokens — it prevents hitting request timeouts. Use the SDK's .get_final_message() / .finalMessage() helper to get the complete response if you don't need to handle individual stream events
Language Detection
Before reading code examples, determine which language the user is working in:
-
Look at project files to infer the language:
*.py,requirements.txt,pyproject.toml,setup.py,Pipfile→ Python — read frompython/*.ts,*.tsx,package.json,tsconfig.json→ TypeScript — read fromtypescript/*.js,*.jsx(no.tsfiles present) → TypeScript — JS uses the same SDK, read fromtypescript/*.java,pom.xml,build.gradle→ Java — read fromjava/*.kt,*.kts,build.gradle.kts→ Java — Kotlin uses the Java SDK, read fromjava/*.scala,build.sbt→ Java — Scala uses the Java SDK, read fromjava/*.go,go.mod→ Go — read fromgo/*.rb,Gemfile→ Ruby — read fromruby/*.cs,*.csproj→ C# — read fromcsharp/*.php,composer.json→ PHP — read fromphp/
-
If multiple languages detected (e.g., both Python and TypeScript files):
- Check which language the user's current file or question relates to
- If still ambiguous, ask: "I detected both Python and TypeScript files. Which language are you using for the Claude API integration?"
What ships with it
25 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.
- csharp/claude-api.md 1.7 KB
- curl/examples.md 4.4 KB
- go/claude-api.md 3.9 KB
- java/claude-api.md 3.8 KB
- LICENSE.txt 11 KB
- php/claude-api.md 1.5 KB
- python/agent-sdk/patterns.md 8.0 KB
- python/agent-sdk/README.md 9.6 KB
- python/claude-api/batches.md 4.8 KB
- python/claude-api/files-api.md 3.8 KB
- python/claude-api/README.md 11 KB
- python/claude-api/streaming.md 5.1 KB
- python/claude-api/tool-use.md 16 KB
- ruby/claude-api.md 1.8 KB
- shared/error-codes.md 6.9 KB
- shared/live-sources.md 10 KB
- shared/models.md 4.5 KB
- shared/tool-use-concepts.md 14 KB
- typescript/agent-sdk/patterns.md 3.2 KB
- typescript/agent-sdk/README.md 7.7 KB
- typescript/claude-api/batches.md 2.5 KB
- typescript/claude-api/files-api.md 2.2 KB
- typescript/claude-api/README.md 8.4 KB
- typescript/claude-api/streaming.md 5.5 KB
- typescript/claude-api/tool-use.md 13 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.
- 3d ago First seen · 244 lines · 84 tokens per session scan A 1ec3d558d336
claude-api is a skill published in the GitHub repository datascienceworld-kan/vinagent (74 stars, last pushed 5mo ago), licensed MIT. It adds 84 tokens to every session and 4,382 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to claude-api, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
prompt-writing
Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.
data-analyzer
Guidance for analyzing structured data, generating statistics and producing data-driven insights. Use when the user asks to analyze data, compute statistics, find patterns, or generate analytical reports.
agents-meet-rl
Troubleshooter for agentic-RL training, evaluation, and experiment design on LLM agents (single or multi-agent, multi-turn, tool-augmented). Routes a user's symptom to fixes anchored in the corpus. TRIGGER when: user is training, evaluating, or designing experiments for an RL-trained LLM agent; symptoms like reward…
prompt-linter
Checks new prompt versions for unsafe patterns, format drift, registry consistency.
agent-data-ml-model
Agent skill for data-ml-model - invoke with $agent-data-ml-model.
agent-neural-network
Agent skill for neural-network - invoke with $agent-neural-network.