DeepSeek Reasoning Tier Classifier

Purpose

• Analyze the current task prompt for complexity signals
• Check conversation history for related context or prior decisions
• Optionally inspect referenced codebase files to assess technical scope
• Map task characteristics to a DeepSeek reasoning tier recommendation
• Present the recommendation with clear rationale and request confirmation if overriding user preference

Use this skill when:

• User explicitly asks which reasoning tier to use
• Task prompt is ambiguous about required reasoning depth
• You detect a mismatch between user-specified tier and task complexity
• Before executing a high-cost max tier request on a potentially trivial task

Do NOT use me for:

• Simple factual questions with no reasoning component
• Tasks where user has already confirmed their tier choice
• Non-DeepSeek model sessions

Requirements

• Any DeepSeek model that supports reasoning tiers is available;

Instructions

Step 1: Gather context

1. Read the latest user message and identify the core task
2. Scan the last 5-10 messages for:
   • Prior tier selections or preferences
   • Related code snippets or file references
   • User statements about speed vs accuracy priorities
3. If the task references specific files, read them to assess:
   • Codebase size and structure
   • Presence of complex logic, async operations, or external integrations
   • Error handling requirements

Step 2: Classify task signals

Use these heuristics to assign a preliminary tier:

Step 3: Validate against constraints

Before finalizing recommendation:

• If task involves file I/O or external APIs, ensure tier supports tool use
• If user has low latency requirements, cap at medium unless they confirm otherwise
• If using V4 Flash, note that high/max tiers hit ~12k thinking token ceiling

Output format

Present your recommendation using this structure:

## Reasoning tier recommendation

**Task**: [brief summary]

**Analysis**:
- Complexity signals: [list key observations]
- Context notes: [relevant history or file insights]
- Constraints: [latency, model, or user prefs]

**Recommended tier**: `low` | `medium` | `high` | `max`

**Rationale**: [1-2 sentences explaining the mapping]

**Next step**:
- If tier matches user request: "Proceed with `{{tier}}` reasoning."
- If tier differs: "Your request specified `{{user_tier}}`, but analysis suggests `{{recommended_tier}}`. Switch? (y/n)"

Limitations

• DeepSeek API does not support dynamic tier switching mid-generation. Any change requires a new API call.
• V4 Flash caps effective thinking tokens at ~12k. Route high/max tasks to V4 Pro if available.
• User may have cost sensitivity. Always surface the cost/latency tradeoff when recommending a higher tier.
• If conversation context is sparse, default to medium and ask clarifying questions rather than guessing max.

Examples

User: "[calls the skill] Write a small bash script to search for files with grep where 'import' appears"

1. Detect keywords: bash, grep, small → signals low
2. Check history: no prior tier preference found
3. No file references to inspect
4. User did not specify a tier, so no override needed
5. Output recommendation for low with rationale

User: "[calls the skill] Debug this race condition in our WebSocket handler" + attaches websocket.ts

1. Detect keyword: debug, race condition → signals high
2. Read websocket.ts: confirms async state management, multiple event listeners
3. Complexity elevated by file analysis → confirm high or consider max
4. Output recommendation for high with note that max is available if deeper verification is needed

Validation loop

After providing a recommendation:

1. Wait for user confirmation if tier differs from their stated preference
2. If user accepts, proceed with the task using the agreed tier
3. If user rejects, note their choice and proceed with their selected tier
4. Log the decision in conversation for future reference (helps with pattern learning)

Troubleshooting