pick-model ยท diff
git:20260411.33e0546 to git:20260923.7852cb4
25 added, 3 removed. Audit A to A.
---
name: pick-model
description: Recommend optimal Claude model (haiku/sonnet/opus) for a task. Use when user asks "which model", "pick model", "model for", or before starting costly/complex tasks. Covers tech and non-tech tasks.
model: haiku
context: main
---
# Pick Model
Classify user's task โ recommend optimal model with reasoning.
## Instructions
1. Parse task description from `$ARGUMENTS`
2. Classify against decision matrix below
3. Output recommendation using format template
## Decision Matrix
### Technical Tasks
| Model | When to Use |
|---|---|
| ๐ข **Haiku** | Simple transforms, formatting, regex, typo fix, status query, template fill, data extraction, factual lookup (no reasoning), file conversion |
| ๐ก **Sonnet** | Single-file coding, bug fix, code review, moderate debugging, test writing, PR review, standard refactoring, technical docs, API integration (known patterns) |
| ๐ด **Opus** | Multi-file refactor (3+ files), architecture/design decisions, complex debugging (multi-system), framework migration, security audit, novel algorithm design, system design with trade-offs |
### Business & Strategy Tasks
| Model | When to Use |
|---|---|
| ๐ข **Haiku** | Summarization (<2K words), data extraction, status reports, simple translations, template filling, meeting notes formatting |
| ๐ก **Sonnet** | Content creation (blog, email, docs), research summaries, competitive analysis, standard business writing, persuasive proposals, marketing copy, customer communications |
| ๐ด **Opus** | Strategic planning, business model design, M&A analysis, organizational design, change management plans, competitive strategy, market entry decisions, crisis response, stakeholder management (competing interests), long-form reports (>2K words), executive presentations with nuance |
### Creative & Analysis Tasks
| Model | When to Use |
|---|---|
| ๐ข **Haiku** | Basic formatting, simple data viz suggestions, straightforward categorization |
| ๐ก **Sonnet** | Creative writing, brainstorming (single framework), persona development, user research synthesis, A/B test analysis, survey analysis |
| ๐ด **Opus** | Multi-framework brainstorming (SCAMPER + Starbursting + trade-off analysis), cross-session pattern detection, bias identification, retrospective analysis, ethical reasoning, strategic foresight, scenario planning |
### Commands, Skills, Agents
| Model | When to Use |
|---|---|
| ๐ข **Haiku** | Simple conversions (PDF, EPUB), format checks, simple utilities, minimal reasoning |
| ๐ก **Sonnet** | Standard workflows, context management, serialization, most skills/commands (DEFAULT) |
| ๐ด **Opus** | Strategic analysis (brainstorm, retrospectives), multi-framework reasoning, high-stakes decisions, pattern detection across sessions |
## Complexity Escalators
Upgrade one tier if task has ANY of these signals:
### Technical Escalators
- **Ambiguity**: Underspecified requirements, multiple valid interpretations โ +1 tier
- **Scope**: Affects 3+ files/systems/components โ +1 tier
- **Stakes**: Production system, security, data-loss risk, regulatory compliance โ +1 tier
- **Novelty**: No established pattern, novel algorithm, cutting-edge tech โ +1 tier
### Business Escalators
- **Multiple stakeholders**: Competing interests, need to balance trade-offs โ +1 tier
- **Strategic impact**: Long-term consequences, irreversible decisions, organizational change โ +1 tier
- **Political sensitivity**: Layoffs, restructuring, executive communications, crisis โ +1 tier
- **Cross-functional**: Requires synthesis across domains (tech + business + legal) โ +1 tier
### Cognitive Escalators
- **Pattern detection**: Requires analyzing trends across multiple data points/sessions โ +1 tier
- **Bias identification**: Needs to spot blindspots, cognitive biases, assumptions โ +1 tier
- **Ethical reasoning**: Moral ambiguity, fairness considerations, unintended consequences โ +1 tier
- **Multi-framework**: Applying 2+ analytical frameworks simultaneously โ +1 tier
**Cap at Opus.** If multiple escalators apply, still cap at Opus (don't "double upgrade").
## Decision Guidance
**When uncertain between two models:**
- **Haiku vs Sonnet**: Does it require *any* reasoning/judgment? โ Sonnet
- **Sonnet vs Opus**: Are there trade-offs to balance or multiple valid approaches? โ Opus
- **Default rule**: When in doubt, go one tier up (better quality > cost savings)
**Quality vs Cost trade-offs:**
- **Cost-sensitive**: Batch processing, exploratory work, drafts โ prefer lower tier
- **Quality-critical**: Customer-facing, executive, production, irreversible โ prefer higher tier
- **Iteration-friendly**: Can easily retry with higher tier if insufficient โ start lower
**Speed considerations:**
- Haiku is ~3-5x faster than Sonnet, ~10x faster than Opus
- For latency-sensitive workflows (UI feedback, real-time), prefer Haiku/Sonnet
- For batch/async work, speed matters less than quality
## Output Format
```
[emoji] **[Model]** โ [1-line reason]
๐ฐ Cost: [lowest/medium/highest] | โก Speed: [fastest/medium/slowest]
๐ก [Optional: "Consider [other model] if [condition]"]
```
**Example output:**
```
๐ด **Opus** โ Multi-stakeholder strategic decision with trade-offs
๐ฐ Cost: highest | โก Speed: slowest
๐ก Consider Sonnet if this is exploratory (draft) rather than final recommendation
```
## Examples
### Technical Tasks
| Task | Recommendation | Rationale |
|---|---|---|
| "fix typo in README" | ๐ข **Haiku** | Trivial single edit, no reasoning |
| "convert PDF to markdown" | ๐ข **Haiku** | Simple conversion, no decisions |
| "debug flaky integration test" | ๐ก **Sonnet** | Single-system debugging, moderate reasoning |
| "refactor auth across 15 files" | ๐ด **Opus** | Multi-file (3+ escalator) + architectural decisions |
| "design database schema for e-commerce" | ๐ด **Opus** | Architectural decision with trade-offs, long-term impact |
| "plan microservices migration strategy" | ๐ด **Opus** | Complex architectural planning + strategic impact escalator |
### Business & Strategy
| Task | Recommendation | Rationale |
|---|---|---|
| "summarize this meeting transcript" | ๐ข **Haiku** | Simple text transformation, <2K words |
| "extract action items from notes" | ๐ข **Haiku** | Data extraction, no reasoning |
| "write blog post about AI trends" | ๐ก **Sonnet** | Creative writing, moderate reasoning |
| "draft sales proposal for enterprise client" | ๐ก **Sonnet** | Persuasive writing, moderate reasoning |
| "analyze competitor pricing strategy" | ๐ก **Sonnet** | Research/analysis, single framework |
| "plan market entry strategy for Europe" | ๐ด **Opus** | Strategic impact + cross-functional + ambiguity escalators |
| "design organizational restructuring plan" | ๐ด **Opus** | Political sensitivity + multiple stakeholders + strategic impact |
| "M&A due diligence analysis" | ๐ด **Opus** | Strategic stakes + cross-functional synthesis required |
| "crisis communication plan for data breach" | ๐ด **Opus** | Political sensitivity + stakes + multiple stakeholders |
### Creative & Analysis
| Task | Recommendation | Rationale |
|---|---|---|
| "translate paragraph to French" | ๐ข **Haiku** | Simple language transform, no reasoning |
| "brainstorm product names (single session)" | ๐ก **Sonnet** | Creative generation, moderate reasoning |
| "brainstorm with SCAMPER + trade-off analysis" | ๐ด **Opus** | Multi-framework escalator (SCAMPER + weighted scoring) |
| "retrospect: analyze collaboration patterns" | ๐ด **Opus** | Pattern detection + bias identification escalators |
| "identify blindspots in strategy" | ๐ด **Opus** | Bias identification + ethical reasoning escalators |
| "plan 3-day conference with speakers" | ๐ด **Opus** | Complex scheduling + multiple stakeholders + constraints |
### Commands, Skills, Agents
| Task | Recommendation | Rationale |
|---|---|---|
| "command: convert EPUB to markdown" | ๐ข **Haiku** | Simple workflow, minimal reasoning |
| "command: save session context" | ๐ก **Sonnet** | Context management, serialization logic |
| "command: brainstorm with research + SCAMPER" | ๐ด **Opus** | Multi-framework escalator + strategic analysis |
| "command: retrospect domain learnings" | ๐ด **Opus** | Pattern detection across sessions + bias identification |
| "skill: format code with prettier" | ๐ข **Haiku** | Simple deterministic task |
| "skill: standard workflow implementation" | ๐ก **Sonnet** | Standard workflow, moderate reasoning |
| "agent: explore codebase architecture" | ๐ด **Opus** | Complex exploration + architectural synthesis |
## Philosophy
- **Right-size, don't default up** โ the cheapest model that meets quality requirements is the correct choice; upgrading is easy, right-sizing takes discipline.
- **Task type over task size** โ model selection depends on reasoning complexity, not the volume of text or files involved.
- **Tier aliases over model names** โ use fast/balanced/reasoning tiers; specific model names change; tier semantics persist.
- **Escalate explicitly** โ if a lower tier fails, escalate with a documented reason rather than defaulting to the top tier always.
## When to Use
- When a user asks "which model should I use for this task?" before starting work
- When selecting between multiple LLM tiers for an automated agent workflow or pipeline
- When cost vs capability tradeoff needs to be explicit (e.g. batch jobs, production routing)
- When an agent must self-assign a model for a sub-task without human input
- When an existing workflow is over-spending on frontier models for simple tasks
## When Not to Use
- When the model is already fixed by infrastructure constraints (e.g. a provider only offers one model)
- When the task has already been completed and model selection is moot
- When the user is asking about non-Claude providers and needs a cross-vendor comparison tool
- When fine-tuned or domain-specific models are the deciding factor, not tier
- When the decision depends on real-time pricing data not available in this skill
## Anti-Patterns
- **NEVER default to the most powerful model for every task** โ Oversized models inflate costs without quality gain on simple tasks. **Why:** A haiku/flash-class model handles classification and routing at 10x lower cost.
- **NEVER pick a model based on benchmark leaderboards alone** โ Benchmark tasks often differ from production workloads. **Why:** Real task performance depends on prompt structure, context length, and domain specificity.
- **NEVER hardcode model names in agent workflows** โ Providers rename and deprecate models frequently. **Why:** Hardcoded names break silently on deprecation; use model tier aliases (fast/balanced/reasoning).
+
+ ```yaml
+ # BAD - pinned to a specific model id; breaks silently when the vendor retires it
+ routing:
+ summarizer: claude-3-haiku-20240307
+ reviewer: claude-3-opus-20240229
+
+ # GOOD - tier alias resolved to a current model id at runtime
+ routing:
+ summarizer: fast # resolves to whichever model is the current fast tier
+ reviewer: reasoning # resolves to whichever model is the current reasoning tier
+ ```
+
- **NEVER skip escalator checks for ambiguous tasks** โ Underestimating complexity leads to poor output requiring costly reruns. **Why:** A single missed escalator (e.g. multi-stakeholder, security risk) can push a task from Sonnet to Opus quality requirements.
+
+ ```text
+ # BAD - classifies on task label alone, ignores escalator signals
+ Task: "refactor the auth module"
+ -> Sonnet (looks like "single-file coding")
+
+ # GOOD - checks escalators before committing to a tier
+ Task: "refactor the auth module"
+ Escalators checked: touches 3+ files (Scope) + handles credentials (Stakes)
+ -> Opus (two escalators fired; Sonnet would under-resource this)
+ ```
+
- **NEVER conflate speed preference with model tier** โ Choosing Haiku solely for latency on a reasoning-heavy task produces wrong answers. **Why:** Speed and capability are separate dimensions; use the decision matrix first, then consider latency constraints.
## Usage Examples
**Selecting a model for a code review task:**
```bash
# Task: review 200-line TypeScript file for bugs
# Tier: balanced (Sonnet-class) โ reasoning needed but not frontier
# Escalators: none (single file, no production risk flagged)
# Output: model alias + rationale
# -> Recommendation: Sonnet โ single-file code review, moderate reasoning required
```
**Routing a summarization request:**
```bash
# Task: summarize 5 meeting notes into bullet points
# Tier: fast (Haiku-class) โ no complex reasoning needed
# Escalators: none (<2K words, no stakeholder trade-offs)
# -> Recommendation: Haiku โ simple text transformation, no judgment required
```
**Classifying a strategic planning task:**
```bash
# Task: design a market entry strategy for a new region
# Tier: reasoning (Opus-class)
# Escalators: strategic impact + cross-functional synthesis + ambiguity
# -> Recommendation: Opus โ multiple escalators detected (strategic impact, ambiguity, cross-functional)
```
## References
- [Reference](references/reference.md) โ extended decision matrix by file type and domain, cost/latency tradeoffs, edge cases, hybrid task patterns, and common mistakes
-
- </content>
- </invoke>