
Task Scaler
- 11 installs
- 2 repo stars
- Updated March 18, 2026
- masanao-ohba/claude-manifests
Helps with ai & agent building tasks.
About
task-scaler is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- task-scaler
- AI & Agent Building
- AI-coding skill
Task Scaler by the numbers
- 11 all-time installs (skills.sh)
- Ranked #11,769 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 11 |
|---|---|
| repo stars | ★ 2 |
| Last updated | March 18, 2026 |
| Repository | masanao-ohba/claude-manifests ↗ |
What it does
Helps with ai & agent building tasks.
Files
Task Scaler
A technology-agnostic skill for evaluating task complexity and determining appropriate workflow scale.
Core Purpose
Classify tasks into appropriate scale categories to optimize:
- Agent utilization
- Workflow complexity
- Resource allocation
- Token consumption
Scale Classification
Categories
trivial:
description: "Minimal changes with obvious implementation"
characteristics:
- Single-line or few-line changes
- Typo fixes, whitespace corrections
- Simple variable renames
- Clear, obvious modifications
metrics:
lines_of_change: "< 3"
files_affected: 1
complexity_score: "< 5"
workflow:
direct_execution: true
agents_required: 0
rai_required: false
deliverable_evaluation: false
small:
description: "Single-component changes with clear scope"
characteristics:
- Single function implementation
- Bug fix in one file
- Simple feature addition
- Minor refactoring
metrics:
lines_of_change: "3-50"
files_affected: "1-3"
complexity_score: "5-14"
workflow:
direct_execution: false
agents_required: "1-2"
rai_required: false
deliverable_evaluation: true
medium:
description: "Multi-component changes requiring coordination"
characteristics:
- Multiple function implementations
- Cross-file changes
- Feature with multiple components
- Significant refactoring
metrics:
lines_of_change: "50-200"
files_affected: "3-10"
complexity_score: "15-29"
workflow:
direct_execution: false
agents_required: "3-4"
rai_required: true
deliverable_evaluation: true
large:
description: "Architectural changes with system-wide impact"
characteristics:
- Architecture modifications
- New module or service
- Multi-tenant considerations
- System-wide impact
metrics:
lines_of_change: "200+"
files_affected: "10+"
complexity_score: "30+"
workflow:
direct_execution: false
agents_required: "minimum needed"
rai_required: true
deliverable_evaluation: true
full_workflow: trueComplexity Scoring
Scoring Factors
factors:
file_count:
weight: 2
calculation: "2 points per affected file"
dependency_depth:
weight: 3
calculation: "3 points per dependency level"
test_requirement:
weight: 5
calculation: "5 points if tests needed"
user_interaction:
weight: 3
calculation: "3 points if user input needed"
integration_complexity:
weight: 4
calculation: "4 points per external integration"
database_changes:
weight: 5
calculation: "5 points if schema changes"Score Thresholds
thresholds:
trivial: "score < 5"
small: "5 <= score < 15"
medium: "15 <= score < 30"
large: "score >= 30"Classification Algorithm
Step 1: Initial Classification
Parse user request for indicators:
trivial_indicators:
keywords:
- "typo", "fix typo", "correct spelling"
- "whitespace", "formatting"
- "single line", "one line"
patterns:
- Change target is explicit and simple
- No logic changes required
small_indicators:
keywords:
- "add function", "implement method"
- "fix bug", "resolve issue"
- "single component", "one file"
patterns:
- Single component scope
- Clear implementation path
medium_indicators:
keywords:
- "add feature", "implement"
- "multiple components", "create tests"
- "refactor"
patterns:
- Multiple files affected
- Testing required
large_indicators:
keywords:
- "architecture", "system"
- "new module", "new service"
- "multi-tenant", "system-wide"
patterns:
- Architectural decisions needed
- Broad impact scopeStep 2: Complexity Analysis
Analyze for complexity factors:
1. Count estimated files affected
2. Assess dependency depth
3. Determine test requirements
4. Identify user interaction needs
5. Check for external integrations
6. Evaluate database impact
7. Calculate total scoreStep 3: Context Adjustments
adjustments:
scale_up_if:
- "High integration with existing code"
- "Ambiguous requirements"
- "Multiple valid approaches"
- "Security implications"
scale_down_if:
- "Established pattern/template exists"
- "User provided detailed instructions"
- "Similar change done recently"
- "Well-documented requirements"Output Format
scale_evaluation:
task_scale: trivial|small|medium|large
complexity_score: <number>
factors:
file_count: <number>
dependency_depth: <number>
test_required: true|false
user_interaction: true|false
integrations: <number>
database_changes: true|false
reasoning:
initial_classification: "<based on keywords/patterns>"
complexity_analysis: "<factor breakdown>"
context_adjustments: "<any scale changes>"
workflow_recommendation:
agents_required: <number or range>
rai_required: true|false
parallel_possible: true|false
estimated_iterations: <number>Anti-Fragmentation Principles
Minimal Agent Usage
principle: "Use minimum agents needed for task"
by_scale:
trivial: "Direct execution, no agents"
small: "1-2 agents maximum"
medium: "3-4 agents, batch similar work"
large: "Minimum needed, maximize parallel"
anti_pattern: "Using 5+ agents for every task"Purposeful Delegation
valid_delegation_reasons:
- "Specialized skill required"
- "Session isolation needed"
- "Parallel processing benefit"
invalid_delegation_reasons:
- "Just in case"
- "For confirmation"
- "Protocol says so"Batch Similar Tasks
batching_principle: "Group similar tasks for single agent"
example:
bad: "3 file fixes → 3 separate agents"
good: "3 file fixes → 1 agent batch"Integration
Used By
primary_users:
- task-scale-evaluator: "Core skill for scale assessment"
- "/dev-workflow command": "Workflow routing decisions"Best Practices
1. Be Conservative: When uncertain, scale up 2. Consider Context: Same task varies by codebase familiarity 3. Avoid Over-Engineering: Match workflow to actual complexity 4. Review History: Similar tasks inform classification 5. Account for Risk: Security/data tasks scale up