es-toolkit is a JavaScript utility library providing commonly used functions such as debouncing, delaying, chunking, summing, and selecting values, with TypeScript types and a compatibility layer for Lodash. JavaScript and TypeScript developers use it when they need utility code with small bundles and performance-focused implementations, while the catalogue skills support AI coding tools working with the library.
Borrowing it
Nothing to install: this file belongs to toss/es-toolkit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/toss/es-toolkit/main/.claude/skills/issue-review/SKILL.mdgit clone --depth 1 https://github.com/toss/es-toolkitWrote 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/toss/es-toolkit/issue-review)<a href="https://agentmods.dev/skills/toss/es-toolkit/issue-review"><img src="https://agentmods.dev/badge/skills/toss/es-toolkit/issue-review/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/toss/es-toolkit/issue-review"><img src="https://agentmods.dev/badge/skills/toss/es-toolkit/issue-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.00587 |
| Opus 5.5 | $0.00006 | $0.00235 |
| Sonnet 5.5 | $0.00003 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
issue-review 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Review
Fetch recent issues, label unlabeled ones with context, detect duplicates.
Input
$ARGUMENTS — Number of issues (default: 10)
Examples:
/issue-review— 10 most recent open issues/issue-review 20— 20 issues
Workflow
1. Fetch recent issues
gh issue list --repo toss/es-toolkit --state open --limit {count} --json number,title,author,labels,createdAt
2. Deep review per issue
For each issue:
a. Read issue content
gh issue view {number} --repo toss/es-toolkit --json title,body,labels,comments
b. Read related source code
If the issue mentions a specific function:
- Read the function source to understand current behavior
- Read existing tests to see what's covered
- Check if there's already a compat variant
c. Provide context
- Bug reports: Is the reported behavior actually a bug? Or is it by design? Does lodash behave differently?
- Feature requests: Does this align with design principles? Is it replaceable by modern JS? Is it TC39 Stage 3+?
- Type issues: Read the current type signature, assess the proposed change
- Docs: Check what's currently documented vs what's being requested
d. Label if unlabeled
If no labels exist, run /issue-label {number}.
3. Detect duplicates
gh issue list --repo toss/es-toolkit --state all --search "{function name}" --limit 10 --json number,title,state,labels
Group by:
- Same function name in title
- Similar error descriptions
- Same feature being requested
4. Report per issue
### Issue #{number} — {title}
**Label**: {existing or newly applied}
**Context**: {what the function currently does, relevant code snippet}
**Analysis**: {is the request valid? design principle alignment?}
**Duplicates**: {similar issues if any}
**Action**: {label applied / needs discussion / close as wontfix / link to existing PR}
5. Summary
## Issue Review — {date}
| # | Title | Label | Duplicate? | Action |
|---|-------|-------|------------|--------|
- {N} issues reviewed
- {N} newly labeled
- {N} potential duplicates
- {N} actionable bugs
- {N} feature requests
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 · 96 lines · 15 tokens per session scan A 0a3e3f82e914
issue-review is a skill published in the GitHub repository toss/es-toolkit (11,378 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 587 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 2026-08-30.
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