Skill: tlalvarez/auxiliar-solve (version v0.1.0)

--- 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: - Is asked to extract text from PDFs, invoices, NFS-e (Brazilian electronic service invoices), boletos, or receipts - Needs to pick between multiple tools (skills, MCPs, vendor APIs, local binaries) for a task - Hits a capability gap and doesn't know what to install - Wants reproducible eval data with scorecards, not marketing blog posts ## How it works ### Step 1. Install the auxiliar MCP server ```bash 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`. - **Human-readable rankings**: https://auxiliar.ai/solve/ - **Reproducible eval harness**: https://github.com/Tlalvarez/Auxiliar-ai/tree/main/scripts/ocr-walkthrough - **Methodology**: https://github.com/Tlalvarez/Auxiliar-ai/blob/main/docs/proposals/agent-upgrade-engine.md (renamed *solve-engine* 2026-04-23) ## License MIT (skill content). See `auxiliar-mcp` and each ranked candidate for their own licenses — `/solve/` surfaces license info in every candidate record.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)