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/toss/es-toolkit/recommendnpx skills add toss/es-toolkit --skill recommendgit clone --depth 1 https://github.com/toss/es-toolkitWhat 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 | $0.00041 | $0.01127 |
| Opus 5 | $0.00020 | $0.00563 |
| Sonnet 5 | $0.00008 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
recommend 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Function Recommendation
Recommend the most suitable es-toolkit function for the user's needs, grounded in source code and official documentation.
Input
$ARGUMENTS — A description of what the user needs, or a code snippet to analyze.
Why grounding matters
es-toolkit evolves faster than any training data can track. Always verify function existence, entry point, and behavior from the version installed in the user's project. The skill may be installed as a standalone directory, so never assume the es-toolkit repository or a sibling docs directory is available.
Workflow
1. Understand the requirement
Parse $ARGUMENTS to identify:
- What operation the user wants to perform
- What data types are involved (array, object, string, etc.)
- Whether they might need lodash migration help (if so, suggest
/es-toolkit:migrate)
2. Find candidates in the installed package
Run from the package that depends on es-toolkit. In a monorepo this is usually a workspace package, not the repository root. If the project uses Yarn Plug'n'Play, use yarn node instead of node.
Search runtime entry points with this command, replacing merge|assign with terms relevant to the request:
node --input-type=module -e "
import { createRequire } from 'node:module';
const require = createRequire(import.meta.url);
const pattern = /merge|assign/i;
const pkg = require('es-toolkit/package.json');
const entries = Object.keys(pkg.exports)
.filter(subpath => subpath !== './package.json' && !subpath.includes('*'))
.map(subpath => subpath === '.' ? 'es-toolkit' : 'es-toolkit/' + subpath.slice(2));
const modules = await Promise.all(entries.map(entry => import(entry).catch(() => null)));
if (modules.every(module => module == null)) {
console.error('es-toolkit is not resolvable from ' + process.cwd());
process.exit(1);
}
const matches = new Map();
for (let index = 0; index < modules.length; index++) {
const module = modules[index];
if (module == null) continue;
for (const name of Object.keys(module)) {
if (!pattern.test(name)) continue;
matches.set(name, [...(matches.get(name) ?? []), entries[index]]);
}
}
for (const [name, entryPoints] of [...matches].sort()) {
console.log(name.padEnd(24), entryPoints.join(', '));
}
"
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 · 112 lines · 41 tokens per session scan A 9f4a06411664
recommend is a skill published in the GitHub repository toss/es-toolkit (11,320 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 1,127 once invoked, about $0.0002 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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