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Copy file name to clipboardExpand all lines: CHANGELOG.md
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<!-- markdownlint-disable MD024 -->
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All notable changes to the Specify CLI and templates are documented here.
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Recent changes to the Specify CLI and templates are documented here.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.1.5] - Unreleased
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## [0.1.6] - 2026-02-23
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### Fixed
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-**Parameter Ordering Issues (#1641)**: Fixed CLI parameter parsing issue where option flags were incorrectly consumed as values for preceding options
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- Added validation to detect when `--ai` or `--ai-commands-dir` incorrectly consume following flags like `--here` or `--ai-skills`
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- Now provides clear error messages: "Invalid value for --ai: '--here'"
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- Includes helpful hints suggesting proper usage and listing available agents
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- Commands like `specify init --ai-skills --ai --here` now fail with actionable feedback instead of confusing "Must specify project name" errors
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- Added comprehensive test suite (5 new tests) to prevent regressions
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## [0.1.5] - 2026-02-21
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### Fixed
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- Affected agents now work correctly: copilot (`.github/agents/`), opencode (`.opencode/command/`), windsurf (`.windsurf/workflows/`), codex (`.codex/prompts/`), kilocode (`.kilocode/workflows/`), q (`.amazonq/prompts/`), and agy (`.agent/workflows/`)
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- The `install_ai_skills()` function now uses the correct path for all agents instead of assuming `commands/` for everyone
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## [0.1.4] - Unreleased
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## [0.1.4] - 2026-02-20
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### Fixed
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-**Qoder CLI detection**: Renamed `AGENT_CONFIG` key from `"qoder"` to `"qodercli"` to match the actual executable name, fixing `specify check` and `specify init --ai` detection failures
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## [0.1.3] - Unreleased
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## [0.1.3] - 2026-02-20
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### Added
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## [0.0.94] - 2026-02-11
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- Add stale workflow for 180-day inactive issues and PRs (#1594)
Copy file name to clipboardExpand all lines: docs/quickstart.md
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/speckit.implement
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```
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> [!TIP]
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> **Phased Implementation**: For complex projects, implement in phases to avoid overwhelming the agent's context. Start with core functionality, validate it works, then add features incrementally.
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## Detailed Example: Building Taskify
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Here's a complete example of building a team productivity platform:
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/speckit.plan We are going to generate this using .NET Aspire, using Postgres as the database. The frontend should use Blazor server with drag-and-drop task boards, real-time updates. There should be a REST API created with a projects API, tasks API, and a notifications API.
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```
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### Step 6: Validate and Implement
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### Step 6: Define Tasks
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Generate an actionable task list using the `/speckit.tasks` command:
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```bash
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/speckit.tasks
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```
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### Step 7: Validate and Implement
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Have your AI agent audit the implementation plan using `/speckit.analyze`:
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/speckit.implement
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```
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> [!TIP]
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> **Phased Implementation**: For large projects like Taskify, consider implementing in phases (e.g., Phase 1: Basic project/task structure, Phase 2: Kanban functionality, Phase 3: Comments and assignments). This prevents context saturation and allows for validation at each stage.
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## Key Principles
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- **Be explicit** about what you're building and why
"description": "Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.",
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