Estimate

> Port provenance: adapted from the pinned upstream source at 984023ddac0d5e27624f2baacde6105e45de375f under MIT; see the repository parity ledger for the exact path and blob.

Phase 1: Understand the Task

Read the task description from the argument. If the description is too vague to estimate meaningfully, ask for clarification before proceeding.

Read AGENTS.md for project context: tech stack, coding standards, architectural patterns, and any estimation guidelines.

Read relevant design documents from design/gdd/ if the task relates to a documented feature or system.

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Phase 2: Scan Affected Code

Identify files and modules that would need to change:

• Assess complexity (size, dependency count, cyclomatic complexity)
• Identify integration points with other systems
• Check for existing test coverage in the affected areas
• Read past sprint data from production/sprints/ for similar completed tasks and historical velocity

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Phase 3: Analyze Complexity Factors

Code Complexity:

• Lines of code in affected files
• Number of dependencies and coupling level
• Whether this touches core/engine code vs leaf/feature code
• Whether existing patterns can be followed or new patterns are needed

Scope:

• Number of systems touched
• New code vs modification of existing code
• Amount of new test coverage required
• Data migration or configuration changes needed

Risk:

• New technology or unfamiliar libraries
• Unclear or ambiguous requirements
• Dependencies on unfinished work
• Cross-system integration complexity
• Performance sensitivity

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Phase 4: Generate the Estimate

## Task Estimate: [Task Name]
Generated: [Date]

### Task Description
[Restate the task clearly in 1-2 sentences]

### Complexity Assessment

| Factor | Assessment | Notes |
|--------|-----------|-------|
| Systems affected | [List] | [Core, gameplay, UI, etc.] |
| Files likely modified | [Count] | [Key files listed below] |
| New code vs modification | [Ratio] | |
| Integration points | [Count] | [Which systems interact] |
| Test coverage needed | [Low / Medium / High] | |
| Existing patterns available | [Yes / Partial / No] | |

**Key files likely affected:**
- `[path/to/file1]` -- [what changes here]

### Effort Estimate

| Scenario | Days | Assumption |
|----------|------|------------|
| Optimistic | [X] | Everything goes right, no surprises |
| Expected | [Y] | Normal pace, minor issues, one round of review |
| Pessimistic | [Z] | Significant unknowns surface, blocked for a day |

**Recommended budget: [Y days]**

### Confidence: [High / Medium / Low]

[Explain which factors drive the confidence level for this specific task.]

### Risk Factors

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|

### Dependencies

| Dependency | Status | Impact if Delayed |
|-----------|--------|-------------------|

### Suggested Breakdown

| # | Sub-task | Estimate | Notes |
|---|----------|----------|-------|
| 1 | [Research / spike] | [X days] | |
| 2 | [Core implementation] | [X days] | |
| 3 | [Testing and validation] | [X days] | |
| | **Total** | **[Y days]** | |

### Notes and Assumptions
- [Key assumption that affects the estimate]
- [Any caveats about scope boundaries]

Output the estimate with a brief summary: recommended budget, confidence level, and the single biggest risk factor.

This skill is read-only — no files are written. Verdict: COMPLETE — estimate generated.

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Phase 5: Next Steps

• If confidence is Low: recommend a time-boxed spike ($ai-game-studio:prototype) before committing.
• If the task is > 10 days: recommend breaking it into smaller stories via $ai-game-studio:create-stories.
• To schedule the task: run $ai-game-studio:sprint-plan update to add it to the next sprint.

Guidelines

• Always give a range (optimistic / expected / pessimistic), never a single number
• The recommended budget should be the expected estimate, not the optimistic one
• Round to half-day increments — estimating in hours implies false precision for tasks longer than a day
• Do not pad estimates silently — call out risk explicitly so the team can decide

Codex portability

Use the search, file-editing, shell, user-input, and subagent capabilities available in the active Codex surface. Use PowerShell syntax on Windows and POSIX syntax on macOS/Linux; do not require a Unix compatibility layer on Windows. Inherit the active model and permission mode, and do not weaken approval or sandbox boundaries.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: frabcd
• Source: frabcd/codex-ai-game-studio
• License: MIT
• Homepage: https://frabcd.github.io/codex-ai-game-studio/

Install and usage instructions live in the source repository linked above.