# LLM Capability Matching > Use when assigning development tasks to different LLMs or estimating costs for multi-agent work. - Skill: `tomevault-io/llm-capability-matching` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/llm-capability-matching` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/llm-capability-matching/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/llm-capability-matching --- # LLM Capability Matching for Multi-Agent Development Assign tasks to the most suitable LLMs based on **live research**, user budget, and task requirements. > **Do NOT rely on hardcoded model scores.** Models and pricing change frequently. > Always use WebSearch to verify current capabilities before making assignments. > See `references/llm-strengths.md` for the full decision protocol. --- ## Workflow ### Step 1: Ask Available LLMs ``` Which LLMs/tools do you have available? What's your budget constraint? (none / moderate / tight) ``` ### Step 2: Research Current Capabilities (WebSearch-First) For EACH LLM the user mentions: ``` WebSearch: "[Model Name] capabilities benchmarks pricing [current year]" ``` Verify from official sources: - Context window (exact size) - Pricing (input/output per 1M tokens) - Strengths (from benchmarks, not assumptions) - Known limitations **Present findings WITH source URLs. Never guess.** ### Step 3: Categorize Tasks | Category | Prioritize | Avoid | |----------|-----------|-------| | Architecture & system design | Strongest reasoning model | Fast/cheap models | | Backend implementation | Good code + fast iteration | Overkill reasoning | | Frontend / UI | Vision-capable, UI-aware | Code-only models | | Testing | Thorough + cost-effective | Expensive flagship | | Documentation | Large context + clear writing | Small context | | DevOps / CI/CD | Broad knowledge | Narrow specialists | | Refactoring | Code-focused, pattern-aware | Conversational models | ### Step 4: Consider Constraints | Constraint | Strategy | |------------|----------| | Budget limited | Use cheaper models for bulk, flagship for architecture only | | Time critical | Use fastest-responding models | | Quality critical | Use flagship for all phases | | Large codebase | Prioritize largest context window | | Single developer | Skip Phase 4; use one model for everything | ### Step 5: Generate Assignment Matrix ```markdown | Agent ID | LLM | Tasks | Est. Cost | Rationale | |----------|-----|-------|-----------|-----------| | [ID] | [Model - verified] | [Tasks] | [Est - from live pricing] | [Why this model - with source] | ``` --- ## Cost Estimation ### Token Estimates by Task Type | Task Type | Est. Input | Est. Output | |-----------|-----------|------------| | Architecture design | 5,000 | 3,000 | | API endpoint (each) | 2,000 | 1,500 | | React component | 3,000 | 2,000 | | Unit test file | 1,500 | 2,000 | | Integration test | 3,000 | 2,500 | | Documentation page | 2,000 | 3,000 | | Refactor module | 4,000 | 3,000 | ``` Total Cost = Sum(task_input_tokens * input_price + task_output_tokens * output_price) ``` --- ## Session Splitting Strategy | Scenario | Recommendation | |----------|----------------| | > 50K tokens expected | Split into phases | | Context loss risk | Checkpoint every 20K | | Multiple modules | One session per module | | Complex dependencies | Sequential sessions | --- ## Assignment Review Checklist - [ ] All tasks have an assigned LLM - [ ] Cost estimates from **live pricing** (not hardcoded) - [ ] Token estimates reasonable - [ ] Handoff points defined - [ ] Session splitting planned - [ ] User has approved assignments --- ## Anti-Patterns - **Never hardcode model scores** - they change with every release - **Never assume pricing** - always verify current rates via WebSearch - **Never skip research** - "I think Model X is good at Y" is not evidence - **Never ignore user experience** - their hands-on experience > benchmarks --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/ankurjain1121) — claim your Tome and manage your conversions.