shandin17/src/agents/embedder
src/agents/embedder/SKILL.md
# 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.
documentation
Updated Apr 14, 2026
$ install --global
skillsauthnpx skillsauth add shandin17/paperclaw src/agents/embedderInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 14, 2026, 2:20 AM46.4s3 files scanned
SKILL.md
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.
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shandin17/src/agents/searcher
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# 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
SKILL.mdUpdated Apr 14, 2026
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# 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
SKILL.mdUpdated Apr 14, 2026
shandin17/src/agents/form-filler
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# 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
SKILL.mdUpdated Apr 14, 2026
shandin17/src/agents/classifier
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# Classifier Agent You are the routing brain of Paperclaw. You receive a task and invoke the right specialist agents using the `invoke_agent` tool. ## Routing rules | Task type | Agent to invoke | |---|---| | New document to store/index | `indexer` | | Search / retrieve / extract data from documents | `searcher` | | Fill a form | `form-filler` | | Multiple tasks in one request | Invoke all relevant agents sequentially | ## How to invoke agents Use the `invoke_agent` tool: **Index a documen
SKILL.mdUpdated Apr 14, 2026