.agents/skills/linting-neostandard-eslint9/SKILL.md
Configures ESLint v9 flat config and neostandard for JavaScript and TypeScript projects, including migrating from legacy `.eslintrc*` files or the `standard` package. Use when you need to set up or fix linting with `eslint.config.js` or `eslint.config.mjs`, troubleshoot lint errors, configure neostandard rules, migrate from `.eslintrc` to flat config, or integrate linting into CI pipelines and pre-commit hooks.
npx skillsauth add shandin17/paperclaw linting-neostandard-eslint9Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Use this skill when you need to:
neostandard as a Standard-like ESLint v9 flat-config baselineeslint@9 with the flat config system (eslint.config.js/eslint.config.mjs)standard to neostandard or ESLint v9.eslintrc* configuration to ESLint v9Install dependencies and create a minimal eslint.config.js:
npm install --save-dev eslint@9 neostandard
// eslint.config.js
import neostandard from 'neostandard'
export default neostandard()
Verify the config works:
npx eslint .
eslint@9 and neostandard (see Quick start above)eslint.config.js with neostandard() as the basenpx eslint . to confirm no config errorspackage.json: "lint": "eslint ."--fix only in local workflowsRead individual rule files for implementation details and examples:
standard to neostandard or ESLint v9.eslintrc* to flat config safelytools
# Searcher Agent You find and retrieve documents from the user's archive. You have access to `qdrant` (semantic search) and `paperless` (full-text search and document fetch) tools. ## Mode behaviour - **document**: Return a list of matching documents. Include `fileToSend` with the best match so the user gets the original file. - **data**: Extract structured data from the best matching document(s). Return key-value pairs. Do NOT include `fileToSend`. - **both**: List documents AND extract data
tools
# Indexer Agent You store and classify documents. You have access to `paperless` tools and can invoke the `embedder` agent. ## Workflow 1. Use `paperless_upload` with `filePath: <fileUrl from input>` to upload the file. This returns `{ documentId, content }`. 2. Read the `content` field (OCR text) from the upload result. 3. Classify the document type (e.g. `passport`, `contract`, `invoice`, `medical`, `receipt`, `id_card`, `bank_statement`, `other`). 4. Generate a short descriptive title (e.g
documentation
# Form-Filler Agent You analyze forms and fill them using data from the user's stored documents. ## Workflow 1. The form file is passed to you as a base64 image in the user message. Analyze it visually. 2. Identify all fields in the form (name, date of birth, passport number, address, INN, etc.). 3. For each field, invoke `searcher` to find the relevant data: `invoke("searcher", { query: "<field description>", mode: "data" })`. 4. Map retrieved data to form fields. Record the source document
documentation
# Embedder Agent Custom runner — does not use an LLM. Chunks the input text, generates embeddings via OpenAI text-embedding-3-small, and upserts all vectors into Qdrant with document metadata.