sandbox-jmespath

sandbox-jmespath is a skill for Claude Code, Codex from nodetool-ai/nodetool. It costs 21 tokens per session (206 once invoked), scanned A, original, AGPL-3.0.

A JSON query tool that uses JMESPath, a language for selecting and reshaping data inside JSON documents.

In plain words
What is it for?
It helps filter, select, and reshape JSON data in code and agent actions.
Why use it?
It makes it easier to find and extract nested JSON values without writing repeated traversal code.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit It helps filter, select, and reshape JSON data in code and agent actions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nodetool-ai/nodetool/sandbox-jmespath
Install

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.

Any agent
npx skills add nodetool-ai/nodetool --skill sandbox-jmespath
Clone the repo
git clone --depth 1 https://github.com/nodetool-ai/nodetool

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for sandbox-jmespath

README.md
[![agentmods](https://agentmods.dev/badge/skills/nodetool-ai/nodetool/sandbox-jmespath/github.svg)](https://agentmods.dev/skills/nodetool-ai/nodetool/sandbox-jmespath)
Your own site
<a href="https://agentmods.dev/skills/nodetool-ai/nodetool/sandbox-jmespath"><img src="https://agentmods.dev/badge/skills/nodetool-ai/nodetool/sandbox-jmespath/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.

agentmods 80×15 button for sandbox-jmespath

Your own site · 80×15
<a href="https://agentmods.dev/skills/nodetool-ai/nodetool/sandbox-jmespath"><img src="https://agentmods.dev/badge/skills/nodetool-ai/nodetool/sandbox-jmespath.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 206 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00021 $0.00206
Opus 5.5 $0.00008 $0.00082
Sonnet 5.5 $0.00004 $0.00041
Haiku 4.5 $0.00002 $0.00021

Measured 1mo ago against content hash c755e5af258c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sandbox-jmespath 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.

packages/sandbox-packs/sandbox-jmespath/SKILL.md · 27 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Files

What ships with it

1 file 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.

Changes

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.

  1. 1mo ago First seen · 27 lines · 21 tokens per session scan A c755e5af258c

Subscribe to this mod's changes

sandbox-jmespath is a skill published in the GitHub repository nodetool-ai/nodetool (571 stars, last pushed today), licensed AGPL-3.0. It adds 21 tokens to every session and 206 once invoked, about $0.0001 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 2026-09-03.

Related

Other skills, from other repositories

pydantic-ai

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

davila7/claude-code-templates · 32 tokens

pydantic-ai

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

skillmds/skillmd · 32 tokens

typecast

Use TypeCast to turn LLM output into validated, typed JVM values. Reach for it whenever code calls an LLM and then parses/validates the response by hand: extraction of case classes or Java records from text, classification/routing via sealed traits, constraint enforcement with retry, streaming partial JSON into a UI…

Alee02/typecast · 86 tokens

quantizing-models-bitsandbytes

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

davila7/claude-code-templates · 83 tokens

free-llm-apis

Guide users through obtaining and configuring free API keys for LLM providers. Use when the user wants to set up a free LLM API, get a free API key, connect to a free model provider, configure an OpenAI-compatible endpoint at no cost, or asks about free tiers for AI models. Triggers on "free API key", "free LLM", "set…

mnfst/awesome-free-llm-apis · 116 tokens

clawrouter

Hosted-gateway LLM router — save 84% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 88 models from OpenAI, Anthropic, Google, DeepSeek, xAI, Z.AI, and more. 7 free open-weight models included. Also exposes realtime market data…

BlockRunAI/ClawRouter · 222 tokens