alignfirst
Collaborative problem-solving protocols: write technical specifications (spec, or alspec), create implementation plans (plan, or alplan), or use Align-and-Do Protocol (AAD). Also generates PR/MR descriptions (aldescription).
Best use case
alignfirst is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Collaborative problem-solving protocols: write technical specifications (spec, or alspec), create implementation plans (plan, or alplan), or use Align-and-Do Protocol (AAD). Also generates PR/MR descriptions (aldescription).
Teams using alignfirst should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alignfirst/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How alignfirst Compares
| Feature / Agent | alignfirst | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Collaborative problem-solving protocols: write technical specifications (spec, or alspec), create implementation plans (plan, or alplan), or use Align-and-Do Protocol (AAD). Also generates PR/MR descriptions (aldescription).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AlignFirst Guide
## Protocols
Choose the appropriate protocol based on the task:
- **Technical Specification** (_spec_, or _alspec_): Read [spec-protocol.md](spec-protocol.md) to write a technical specification
- **Implementation Plans** (_plan_, or _alplan_): Read [plan-protocol.md](plan-protocol.md) to create implementation plans from a spec
- **Align-and-Do Protocol** (_AAD_): Read [do-protocol.md](do-protocol.md) for smaller tasks without formal spec/plans
- **Description** (_aldescription_): Read [description-protocol.md](description-protocol.md) to write a description summarizing implemented work
## TASK_DIR Location
**TASK_DIR** is the directory where work files related to a task are stored. Usually, we use **TASK_DIR** = `_plans/{TICKET_ID}/` (a sub-directory of the `_plans` folder). If no ticket ID is known, ask the user for it.
- Create TASK_DIR if it doesn't exist
- Or, list existing files
## File Naming Convention
Format: `{CYCLE_LETTER}{FILE_NUMBER}-{FILE_TYPE}.md`
**Common file types:**
- `spec` - technical specification
- `plan` - implementation plan
- `AAD.summary` - AAD summary document
**Example structure:**
```text
_plans/
├── 123/
│ ├── A1-spec.md
│ ├── A2-plan.md
│ └── A3-AAD.summary.md
│ └── B1-spec.md
```
## Notes
- **TICKET_ID** is a unique identifier for the task, often an issue or ticket number.
- Cycles are identified by a **CYCLE_LETTER** (A, B, C...). The user decides when to start a new one.
- In a cycle, determine the next **FILE_NUMBER** from existing file names. Every new file must have a bumped file number.
- Do not bother the user with CYCLE_LETTER or FILE_NUMBER. They are for internal organization. It's up to you to list the files and determine the last CYCLE_LETTER and FILE_NUMBER. Start CYCLE_LETTER with `A` if there is no existing cycle, and FILE_NUMBER with `1`. So you just need to ask for a **ticket ID** if you don't have one.
- When the user requests a new cycle: bump CYCLE_LETTER and reset FILE_NUMBER.
- There is no strict sequence of file types in the workflow. Available file types are also flexible; if you need a new one, just create it.Related Skills
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.
moai-lang-r
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.
moai-lang-python
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.
moai-icons-vector
Vector icon libraries ecosystem guide covering 10+ major libraries with 200K+ icons, including React Icons (35K+), Lucide (1000+), Tabler Icons (5900+), Iconify (200K+), Heroicons, Phosphor, and Radix Icons with implementation patterns, decision trees, and best practices.
moai-foundation-trust
Complete TRUST 4 principles guide covering Test First, Readable, Unified, Secured. Validation methods, enterprise quality gates, metrics, and November 2025 standards. Enterprise v4.0 with 50+ software quality standards references.
moai-foundation-memory
Persistent memory across sessions using MCP Memory Server for user preferences, project context, and learned patterns
moai-foundation-core
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
moai-cc-claude-md
Authoring CLAUDE.md Project Instructions. Design project-specific AI guidance, document workflows, define architecture patterns. Use when creating CLAUDE.md files for projects, documenting team standards, or establishing AI collaboration guidelines.
moai-alfred-language-detection
Auto-detects project language and framework from package.json, pyproject.toml, etc.
mnemonic
Unified memory system - aggregates communications and AI sessions across all channels into searchable, analyzable memory
mlops
MLflow, model versioning, experiment tracking, model registry, and production ML systems
ml-pipeline
Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.