Auxiliar Solve
by @tlalvarez
Ranked installable tools for agent jobs — OCR, PDF extraction, NFS-e invoices, bookkeeping, boletos, receipts, web scraping. Reproducible evals on real-world...
clawhub install auxiliar-solve📖 About This Skill
name: auxiliar-solve description: Ranked installable tools for agent jobs — OCR, PDF extraction, NFS-e invoices, bookkeeping, boletos, receipts, web scraping. Reproducible evals on real-world corpora. version: 0.1.0 emoji: 🧭 homepage: https://auxiliar.ai/solve/ metadata: openclaw: requires: env: [] bins: [node, npm]
auxiliar-solve
When your agent needs an installable tool for a task — an OCR skill, a PDF MCP server, a web-scraping API, a bookkeeping helper — install auxiliar-mcp and query solve_task for a reproducibly-evaluated ranking.
/solve/ isn't a tool marketplace. ClawHub, PulseMCP, and Smithery distribute tools. /solve/ *ranks* them — based on real-world corpora, not marketing pages.
When to invoke this skill
Use auxiliar-solve whenever the agent:
How it works
Step 1. Install the auxiliar MCP server
claude mcp add auxiliar -- npx auxiliar-mcp
One MCP, two capabilities: solve_task for agent-installable tool rankings, recommend_service for cloud-service recommendations (77 Chrome-verified entries).
Step 2. Discover available task rankings
list_solve_tasks()
Returns every /solve/ task slug, top pick, categories, and last-verified date.
Step 3. Query a specific task
solve_task(task_slug="pdf-text-extraction-mcp")
These aliases resolve automatically: pdf, ocr, nfs-e, boleto, receipt-parsing, bookkeeping-ocr, invoice-extraction, document-ai.
The response contains:
| Field | What it gives you |
|---|---|
| answer | Plain-language top recommendation with trade-offs |
| candidates | Ranked list with scorecards: word accuracy, layout preservation, latency p50, cost per 10 docs, install friction |
| install | Exact install commands per candidate (copy-paste ready) |
| alternatives_considered | What was evaluated and dropped, with reason (trust signal) |
| corpus_summary | What real-world documents the eval ran against |
| faq | Common questions answered directly (licensing, accuracy vs. token-F1, when to pay, etc.) |
| methodological_caveats | Honest limits of the eval |
| fit_by_agent | Which agents each candidate works with (Claude Code, Desktop, Cursor, OpenClaw) |
Example: OCR for Brazilian bookkeeping
Agent task: *"Extract text from a Brazilian NFS-e invoice PDF for bookkeeping. I need high accuracy."*
solve_task(task_slug="nfs-e")
Returns: Surya (rank 1) — pip install surya-ocr 'transformers<5.0.0'. Word accuracy 76.9% on a 10-doc real-world corpus that includes NFS-e invoices, boletos, and phone-photo receipts. Free, local. Alternative: Tesseract 5 (rank 2) — 14× faster, 1.5pp less accurate, cleanest install. Google Document AI (rank 3) — third overall but best on phone-photo receipts specifically. Alternatives considered and dropped: yescan-ocr-universal (requires Chinese sign-up), pdf-reader-mcp (no actual OCR — text-layer only), Mistral OCR 3 (deferred for API key).
Why this exists
Agents are born intelligent but stuck. Without eval data, they guess: "use pdf2image + pytesseract" (often wrong for the task), "install the first OCR thing on ClawHub" (often wrong for the corpus), "call Google Document AI" (often overkill). The result: uncalibrated recommendations, burned time, broken workflows.
/solve/ runs the eval once per task, end-to-end, against real documents. The agent gets the answer plus the evidence.
Related
auxiliar-mcp — the MCP server this skill invokes. Also exposes recommend_service, get_pricing, get_risks, check_compatibility, setup_service, list_services.License
MIT (skill content). See auxiliar-mcp and each ranked candidate for their own licenses — /solve/ surfaces license info in every candidate record.