Complexity Assessment

Complexity Assessment Skill

Overview

Analyze a task description and determine its true complexity to ensure the right workflow and validation depth are selected. Accuracy over speed - wrong complexity means wrong workflow means failed implementation.

Core principle: Accuracy over speed. Wrong complexity = wrong workflow = failed implementation.

When to Use

Always:

• Before planning any new task
• When requirements are gathered but approach unclear
• When determining validation depth for QA

Exceptions:

• Obvious simple fixes (typos, color changes)
• Tasks where complexity is explicitly specified

Iron Laws

1. NEVER begin planning without complexity assessment — wrong complexity tier = wrong workflow = failed implementation; always assess first, then select the appropriate workflow phases and validation depth. 2. ALWAYS be conservative when uncertain — go higher — underestimating complexity is more dangerous than overestimating; a COMPLEX task mis-classified as STANDARD skips security review, architecture design, and comprehensive testing. 3. ALWAYS count affected files before assigning a tier — gut-feel estimates are unreliable; scan the codebase to count files actually touched by the change before assigning SIMPLE/STANDARD/COMPLEX/EPIC. 4. ALWAYS flag unfamiliar technologies for research — unknown tech has hidden complexity; mark any unfamiliar framework, library, or service as requiring research before proceeding with the complexity estimate. 5. NEVER let the user's casual language lower the tier — "just a quick fix" or "small change" reflects the user's perception, not the actual technical scope; assess objectively regardless of how it is described.

Workflow Types

Determine the type of work being requested:

FEATURE

• Adding new functionality to the codebase
• Enhancing existing features with new capabilities
• Building new UI components, API endpoints, or services
• Examples: "Add screenshot paste", "Build user dashboard", "Create new API endpoint"

REFACTOR

• Replacing existing functionality with a new implementation
• Migrating from one system/pattern to another
• Reorganizing code structure while preserving behavior
• Examples: "Migrate auth from sessions to JWT", "Refactor cache layer"

INVESTIGATION

• Debugging unknown issues
• Root cause analysis for bugs
• Performance investigations
• Examples: "Find why page loads slowly", "Debug intermittent crash"

MIGRATION

• Data migrations between systems
• Database schema changes with data transformation
• Import/export operations
• Examples: "Migrate user data to new schema", "Import legacy records"

SIMPLE

• Very small, well-defined changes
• Single file modifications
• No architectural decisions needed
• Examples: "Fix typo", "Update button color", "Change error message"

Complexity Tiers

SIMPLE

• 1-2 files modified
• Single service/area
• No external integrations
• No infrastructure changes
• No new dependencies
• Examples: typo fixes, color changes, text updates, simple bug fixes

STANDARD

• 3-10 files modified
• 1-2 services/areas
• 0-1 external integrations (well-documented, simple to use)
• Minimal infrastructure changes (e.g., adding an env var)
• May need some research but core patterns exist
• Examples: adding a new API endpoint, creating a new component

COMPLEX

• 10+ files OR cross-cutting changes
• Multiple services/areas
• 2+ external integrations
• Infrastructure changes (Docker, databases, queues)
• New architectural patterns
• Greenfield features requiring research
• Examples: new integrations (Stripe, Auth0), database migrations, new services

Workflow

Phase 1: Load Requirements

Read the requirements document:

# Read the requirements file
cat .claude/context/requirements/[task-name].md

Extract:

• task_description: What needs to be built
• workflow_type: Type of work (feature, refactor, etc.)
• scope: Which areas are affected
• requirements: Specific requirements
• acceptance_criteria: How success is measured
• constraints: Any limitations

Phase 2: Analyze the Task

Read the task description carefully. Look for:

Complexity Indicators (suggest higher complexity):

• "integrate", "integration" - external dependency
• "optional", "configurable", "toggle" - feature flags, conditional logic
• "docker", "compose", "container" - infrastructure
• Database names (postgres, redis, mongo) - infrastructure + config
• API/SDK names (stripe, auth0, openai) - external research needed
• "migrate", "migration" - data/schema changes
• "across", "all services", "everywhere" - cross-cutting
• "new service" - significant scope
• ".env", "environment", "config" - configuration complexity

Simplicity Indicators (suggest lower complexity):

• "fix", "typo", "update", "change" - modification
• "single file", "one component" - limited scope
• "style", "color", "text", "label" - UI tweaks
• Specific file paths mentioned - known scope

Phase 3: Assess Dimensions

Scope Analysis

• How many files will likely be touched?
• How many areas are involved?
• Is this a localized change or cross-cutting?

Integration Analysis

• Does this involve external services/APIs?
• Are there new dependencies to add?
• Do these dependencies require research?

Infrastructure Analysis

• Does this require Docker/container changes?
• Does this require database schema changes?
• Does this require new environment configuration?

Knowledge Analysis

• Does the codebase already have patterns for this?
• Will research be needed for external docs?
• Are there unfamiliar technologies involved?

Risk Analysis

• What could go wrong?
• Are there security considerations?
• Could this break existing functionality?

Phase 4: Determine Phases Needed

Based on your analysis, determine which phases are needed:

For SIMPLE tasks:

discovery → quick_spec → validation

(3 phases, no research, minimal planning)

For STANDARD tasks:

discovery → requirements → context → spec_writing → planning → validation

(6 phases, context-based spec writing)

For STANDARD tasks WITH external dependencies:

discovery → requirements → research → context → spec_writing → planning → validation

(7 phases, includes research for unfamiliar dependencies)

For COMPLEX tasks:

discovery → requirements → research → context → spec_writing → self_critique → planning → validation

(8 phases, full pipeline with research and self-critique)

Phase 5: Determine Validation Depth

Based on complexity and risk analysis, recommend validation depth:

Skip Validation Criteria (TRIVIAL): Set only when ALL are true:

• Documentation-only changes (\*.md, comments, docstrings)
• OR purely cosmetic (whitespace, formatting, linting fixes)
• No functional code modified
• Confidence >= 0.9

Security Scan Required when ANY apply:

• Authentication/authorization code touched
• User data handling modified
• Payment/financial code involved
• API keys, secrets, or credentials handled
• New dependencies with network access
• File upload/download functionality

Phase 6: Output Assessment

Create the structured assessment:

# Complexity Assessment: [Task Name]

## Summary

| Dimension     | Assessment                                        |
| ------------- | ------------------------------------------------- |
| Complexity    | [simple/standard/complex]                         |
| Workflow Type | [feature/refactor/investigation/migration/simple] |
| Confidence    | [0.0-1.0]                                         |

## Reasoning

[2-3 sentence explanation]

## Analysis

### Scope

- Estimated files: [number]
- Estimated areas: [number]
- Cross-cutting: [yes/no]
- Notes: [brief explanation]

### Integrations

- External services: [list]
- New dependencies: [list]
- Research needed: [yes/no]
- Notes: [brief explanation]

### Infrastructure

- Docker changes: [yes/no]
- Database changes: [yes/no]
- Config changes: [yes/no]
- Notes: [brief explanation]

### Knowledge

- Patterns exist: [yes/no]
- Research required: [yes/no]
- Unfamiliar tech: [list]
- Notes: [brief explanation]

### Risk

- Level: [low/medium/high]
- Concerns: [list]
- Notes: [brief explanation]

## Recommended Phases

1. [phase1]
2. [phase2]
3. ...

## Validation Recommendations

| Setting          | Value                              |
| ---------------- | ---------------------------------- |
| Risk Level       | [trivial/low/medium/high/critical] |
| Skip Validation  | [yes/no]                           |
| Minimal Mode     | [yes/no]                           |
| Test Types       | [unit, integration, e2e]           |
| Security Scan    | [yes/no]                           |
| Staging Required | [yes/no]                           |

**Reasoning**: [1-2 sentences explaining validation depth]

## Flags

- Needs research: [yes/no]
- Needs self-critique: [yes/no]
- Needs infrastructure setup: [yes/no]

Decision Flowchart

START
  |
  +--> Are there 2+ external integrations OR unfamiliar technologies?
  |     YES -> COMPLEX (needs research + critique)
  |     NO
  |      |
  +--> Are there infrastructure changes (Docker, DB, new services)?
  |     YES -> COMPLEX (needs research + critique)
  |     NO
  |      |
  +--> Is there 1 external integration that needs research?
  |     YES -> STANDARD + research phase
  |     NO
  |      |
  +--> Will this touch 3+ files across 1-2 areas?
  |     YES -> STANDARD
  |     NO
  |      |
  +--> SIMPLE (1-2 files, single area, no integrations)

Verification Checklist

Before completing assessment:

• [ ] Requirements document read completely
• [ ] All complexity indicators identified
• [ ] All simplicity indicators identified
• [ ] Scope analyzed (files, areas, cross-cutting)
• [ ] Integrations analyzed (external, dependencies)
• [ ] Infrastructure needs assessed
• [ ] Knowledge gaps identified
• [ ] Risk level determined
• [ ] Phases determined
• [ ] Validation depth recommended

Common Mistakes

Underestimating Integrations

Why it's wrong: One integration can touch many files.

Do this instead: Flag research needs for any unfamiliar technology.

Ignoring Infrastructure

Why it's wrong: Docker/DB changes add significant complexity.

Do this instead: Check for infrastructure needs early.

Over-Confident

Why it's wrong: Rarely should confidence be above 0.9.

Do this instead: Be conservative. When in doubt, go higher complexity.

Integration with Other Skills

This skill works well with:

• spec-gathering: Provides requirements for assessment
• spec-writing: Uses assessment to determine spec depth
• qa-workflow: Uses validation recommendations

Anti-Patterns

Memory Protocol

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New pattern -> .claude/context/memory/learnings.md
• Issue found -> .claude/context/memory/issues.md
• Decision made -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

Invoke the complexity-assessment skill and follow it exactly as presented to you

complexity-assessment Research Requirements

Generated: 2026-02-28

Skill Description

'AI-based complexity assessment for task analysis. Use when determining the appropriate workflow, phases, and validation depth for a task.'

Research Areas

• Current best practices for complexity-assessment
• Industry standards and tooling
• Integration patterns

Source References

• To be populated by skill-updater research phase

complexity-assessment Rules

Purpose

'AI-based complexity assessment for task analysis. Use when determining the appropriate workflow, phases, and validation depth for a task.'

Best Practices

• Be conservative - when in doubt, go higher complexity
• Flag research needs for unfamiliar technologies
• Consider hidden complexity in optional features

Integration Points

See SKILL.md for complete documentation.

complexity-assessment Implementation Template

Goal

• Define target outcome and acceptance criteria.

TDD

1. Red 2. Green 3. Refactor

Verification

• lint
• format
• targeted tests