framework/devtools/skills/flowai-skill-engineer-command/SKILL.md
Guide for creating effective flowai commands. This skill should be used when users want to create a new command (or update an existing command) that extends flowai's capabilities with specialized knowledge, workflows, or tool integrations. Works across IDEs (Cursor, Claude Code, OpenCode).
npx skillsauth add korchasa/flowai flowai-skill-engineer-commandInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides guidance for creating effective flowai commands.
When asking the user a choice (IDE, scope, examples to cover):
1., 2., …) — not a heading, bold-only line, or paragraph.agent's choice (or equivalent), pick the subset yourself, emit a one-line justification of the pick, and proceed without re-asking for confirmation.Commands are modular, self-contained packages that extend flowai's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform flowai from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.
Commands work across multiple IDEs. Before creating a command, determine the current environment and ask the user where to place it.
| IDE | User Commands | Project Commands | Format |
|-----|--------------|-----------------|--------|
| Cursor | ~/.cursor/commands/*.md | .cursor/commands/*.md | Markdown (free-form args) |
| Claude Code | ~/.claude/commands/*.md | .claude/commands/*.md<br>.claude/commands/<namespace>/*.md | Markdown + YAML frontmatter (allowed-tools, argument-hint, description, model) |
| OpenCode | ~/.config/opencode/commands/*.md | .opencode/commands/*.md | Markdown + YAML frontmatter (description, agent, model, subtask) |
Note: Claude Code unifies commands and skills.
.claude/commands/is the legacy path;.claude/skills/(SKILL.md format) is recommended for new commands.
OpenCode supports $ARGUMENTS, $1-$N, !`shell command`, @filepath in command templates.
.cursor/ directory → Cursor.claude/ directory → Claude Code.opencode/ directory or opencode.json → OpenCodeThe context window is a public good. Commands share the context window with everything else flowai needs: system prompt, conversation history, other Commands' metadata, and the actual user request.
Default assumption: flowai is already very smart. Only add context flowai doesn't already have. Challenge each piece of information: "Does flowai really need this explanation?" and "Does this paragraph justify its token cost?"
Prefer concise examples over verbose explanations.
Match the level of specificity to the task's fragility and variability:
High freedom (text-based instructions): Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
Medium freedom (pseudocode or scripts with parameters): Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
Low freedom (specific scripts, few parameters): Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
Think of flowai as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
Every command consists of a required SKILL.md file and optional bundled resources, located in .cursor/skills/cmd-<name>:
cmd-<name>/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
Every SKILL.md consists of:
name and description fields. These are the only fields that flowai reads to determine when the command gets used, thus it is very important to be clear and comprehensive in describing what the command is, and when it should be used.scripts/)Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
scripts/rotate_pdf.py for PDF rotation tasksreferences/)Documentation and reference material intended to be loaded as needed into context to inform flowai's process and thinking.
references/finance.md for financial schemas, references/mnda.md for company NDA template, references/policies.md for company policies, references/api_docs.md for API specificationsassets/)Files not intended to be loaded into context, but rather used within the output flowai produces.
assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates, assets/frontend-template/ for HTML/React boilerplate, assets/font.ttf for typographyA command should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
The command should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
Commands use a three-level loading system to manage context efficiently:
Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the command knows they exist and when to use them.
Key principle: When a command supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.
Pattern 1: High-level guide with references
# PDF Processing
## Quick start
Extract text with pdfplumber:
[code example]
## Advanced features
- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
flowai loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
Pattern 2: Domain-specific organization
For Commands with multiple domains, organize content by domain to avoid loading irrelevant context:
flowai-bigquery/
├── SKILL.md (overview and navigation)
└── reference/
├── finance.md (revenue, billing metrics)
├── sales.md (opportunities, pipeline)
├── product.md (API usage, features)
└── marketing.md (campaigns, attribution)
When a user asks about sales metrics, flowai only reads sales.md.
Similarly, for commands supporting multiple frameworks or variants, organize by variant:
flowai-cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
├── aws.md (AWS deployment patterns)
├── gcp.md (GCP deployment patterns)
└── azure.md (Azure deployment patterns)
When the user chooses AWS, flowai only reads aws.md.
Pattern 3: Conditional details
Show basic content, link to advanced content:
# DOCX Processing
## Creating documents
Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).
## Editing documents
For simple edits, modify the XML directly.
**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)
flowai reads REDLINING.md or OOXML.md only when the user needs those features.
Important guidelines:
Command creation involves these steps:
Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
Skip this step only when the command's usage patterns are already clearly understood. It remains valuable even when working with an existing command.
To create an effective command, clearly understand concrete examples of how the command will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
For example, when building an image-editor command, relevant questions include:
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
Conclude this step when there is a clear sense of the functionality the command should support.
To turn concrete examples into an effective command, analyze each example by:
Example: When building a flowai-pdf-editor command to handle queries like "Help me rotate this PDF," the analysis shows:
scripts/rotate_pdf.py script would be helpful to store in the commandExample: When designing a flowai-webapp-builder command for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
assets/hello-world/ template containing the boilerplate HTML/React project files would be helpful to store in the commandExample: When building a flowai-big-query command to handle queries like "How many users have logged in today?" the analysis shows:
references/schema.md file documenting the table schemas would be helpful to store in the commandTo establish the command's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
At this point, it is time to actually create the command.
Skip this step only if the command being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new command from scratch, always run the init_command.ts script. The script conveniently generates a new template command directory that automatically includes everything a command requires, making the command creation process much more efficient and reliable.
Usage:
deno run -A scripts/init_command.ts <command-name> --path <output-directory>
The script:
scripts/, references/, and assets/After initialization, customize or remove the generated SKILL.md and example files as needed.
When editing the (newly-generated or existing) command, remember that the command is being created for another instance of flowai to use. Include information that would be beneficial and non-obvious to flowai. Consider what procedural knowledge, domain-specific details, or reusable assets would help another flowai instance execute these tasks more effectively.
Consult these helpful guides based on your command's needs:
These files contain established best practices for effective command design.
To begin implementation, start with the reusable resources identified above: scripts/, references/, and assets/ files. Note that this step may require user input. For example, when implementing a brand-guidelines command, the user may need to provide brand assets or templates to store in assets/, or documentation to store in references/.
Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
Any example files and directories not needed for the command should be deleted. The initialization script creates example files in scripts/, references/, and assets/ to demonstrate structure, but most commands won't need all of them.
Writing Guidelines: Always use imperative/infinitive form.
Write the YAML frontmatter with name and description:
name: The command namedescription: This is the primary triggering mechanism for your command, and helps flowai understand when to use the command.
docx command: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when flowai needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"Do not include any other fields in YAML frontmatter.
Write instructions for using the command and its bundled resources.
Once development of the command is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the command first to ensure it meets all requirements:
deno run -A scripts/package_command.ts <path/to/command-folder>
Optional output directory specification:
deno run -A scripts/package_command.ts <path/to/command-folder> ./dist
The packaging script will:
Validate the command automatically, checking:
Package the command if validation passes, creating a .skill file named after the command (e.g., flowai-my-command.skill) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
After testing the command, users may request improvements. Often this happens right after using the command, with fresh context of how the command performed.
Iteration workflow:
development
Use when the user asks to add TypeScript strict-mode code-style rules to AGENTS.md for a TypeScript project using strict mode. Do NOT trigger for Deno projects (use setup-agent-code-style-deno) or non-strict TS configurations.
development
Use when the user asks to add Deno/TypeScript code-style rules to AGENTS.md, or during initial Deno project setup when code-style guidelines need to be established. Do NOT trigger for non-Deno TypeScript projects (use setup-agent-code-style-strict), or for runtime-agnostic style advice.
testing
Use when the user provides a source (URL, file path, or free text) to save into the project's memex — a long-term knowledge bank for AI agents. Stores the raw source, extracts entities into cross-linked pages, runs a backlink audit, and updates the index and activity log. Do NOT trigger on casual reads; only when the intent is to persist a source into the memex.
development
Use when the user asks to audit a memex (long-term knowledge bank for AI agents) for orphans, dead SALP REFs, missing sections, contradictions, or index drift. Runs a deterministic structural check, layers LLM-judgement findings, optionally auto-fixes trivial issues with `--fix`. Do NOT trigger on general code linting.