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 agentsope/SkillAlchemy --skill agentsop-http-tool-wrappinggit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-http-tool-wrapping)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-http-tool-wrapping"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-http-tool-wrapping/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/agentsope/skillalchemy/agentsop-http-tool-wrapping"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-http-tool-wrapping.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 Tool Misuse · line 86 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00155 | $0.06312 |
| Opus 5.5 | $0.00062 | $0.02525 |
| Sonnet 5.5 | $0.00031 | $0.01262 |
| Haiku 4.5 | $0.00015 | $0.00631 |
Grade A, and why
agentsop-http-tool-wrapping 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 1mo 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 — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HTTP / External API → Agent Tool · SOP
Source posture: every non-trivial claim is cited inline with short tags like
[oai/fc],[anthropic/tooluse],[lc/tools],[mcp/spec],[apxml/schema]. Resolve them againstreferences/R1-source-evidence.mdfor full URLs. Reusable code shapes live inreferences/R2-pattern-library.md.
1. 何时激活 (When to Activate)
Activate when a coder agent must make an external HTTP API callable by an LLM. Concrete triggers:
- The task says "give the agent access to ", "add a tool that calls ", "wrap our REST/GraphQL/RPC endpoint as a function the model can use".
- You are choosing which of N endpoints become tools, or how to name them.
- An existing tool returns a huge JSON blob and the model hallucinates field names, or burns context re-reading it.
- Tool calls die on
429, timeouts, or unpaginated list endpoints. - You need the same tool to run under OpenAI function calling, Anthropic
tool_use, an MCP server, LangChain@tool, and CrewAIBaseTool.
Do not activate when: the API is already exposed as an MCP server you merely consume (just connect it); the "tool" is pure local computation with no network I/O (write a plain typed function); or you are designing the upstream API itself.
This is a tool-construction skill — sibling to the framework SOPs
(langgraph-sop, crewai-sop) which decide whether/where tools run. Once you
know you need a tool, this skill decides what shape it takes.
2. 核心心智模型 (Core Mental Model)
The tool surface is an LM-friendly subset of the API surface. One tool per intent, not one per endpoint.
A REST API is designed for programmers who read docs, hold a mental model of resources, and compose calls. An agent tool is designed for a language model that sees only a name, a description, and a JSON schema — and must decide, mid-reasoning, whether this is the thing to call. These are different audiences, so the surface must be re-cut, not mirrored.
What ships with it
4 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.
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.
- 1mo ago First seen · 453 lines · 155 tokens per session scan A 22f2dc5d08b4
agentsop-http-tool-wrapping is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 155 tokens to every session and 6,312 once invoked, about $0.0006 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.
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