generate
npx machina-cli add skill synaptiai/agent-capability-standard/generate --openclawIntent
Produce a new artifact that satisfies specified constraints and serves a defined purpose. Generation is creative synthesis bounded by requirements, not mere retrieval or transformation.
Success criteria:
- Artifact satisfies all stated constraints
- Rationale explains design decisions
- Quality signals demonstrate fitness for purpose
- Safety considerations documented
Compatible schemas:
schemas/output_schema.yaml
Inputs
| Parameter | Required | Type | Description |
|---|---|---|---|
artifact_type | Yes | string | Type of output: text, code, plan, config, schema, etc. |
constraints | Yes | object|array | Requirements the artifact must satisfy |
context | No | string|object | Background information, examples, or references |
format | No | string | Output format: markdown, json, yaml, typescript, etc. |
alternatives_requested | No | boolean | Whether to provide alternative designs |
Procedure
-
Clarify artifact requirements: Ensure constraints are understood
- Must-have: absolute requirements (format, structure, compatibility)
- Should-have: preferences (style, conventions, optimization)
- Must-not-have: exclusions (patterns to avoid, security constraints)
-
Gather context: Collect relevant reference material
- Read existing code/docs for style consistency
- Identify patterns to follow or avoid
- Understand integration points and dependencies
-
Design approach: Plan the generation strategy
- Outline structure before detailed generation
- Consider multiple approaches if request is complex
- Document key design decisions
-
Generate artifact: Produce the content systematically
- Follow identified patterns and conventions
- Satisfy constraints in priority order
- Document any constraints that conflict
-
Validate constraints: Verify each constraint is satisfied
- Mark each constraint as satisfied/unsatisfied
- Note any compromises or partial satisfaction
-
Assess quality: Evaluate fitness for purpose
- Correctness: Does it work as intended?
- Completeness: Is anything missing?
- Maintainability: Is it understandable and modifiable?
- Safety: Are there security or reliability concerns?
-
Document rationale: Explain why this design
- Key decisions and their reasoning
- Alternatives considered and why rejected
- Known limitations or future considerations
-
Ground claims: Reference sources that informed generation
Output Contract
Return a structured object:
artifact:
type: string # text, code, plan, config, schema, etc.
content: string | object # The generated artifact
format: string # markdown, json, yaml, typescript, etc.
constraints_used:
- constraint: string # Constraint description
satisfied: boolean # Was it met?
notes: string # How it was satisfied or why not
rationale: string # Why this design/approach
alternatives_considered: array[string] # Other approaches evaluated
quality_signals:
- metric: string # Quality dimension
value: number | string # Assessment
confidence: number # 0.0-1.0 in artifact quality
evidence_anchors: array[string] # References to source material
assumptions: array[string] # Conditions affecting the artifact
safety_notes: array[string] # Security/reliability considerations
next_actions: array[string] # Suggested follow-ups
Field Definitions
| Field | Type | Description |
|---|---|---|
artifact.type | string | Category of generated content |
artifact.content | string|object | The generated artifact itself |
constraints_used | array | Evaluation of constraint satisfaction |
rationale | string | Design decision explanation |
alternatives_considered | array | Other approaches that were rejected |
quality_signals | array | Quality metrics and assessments |
confidence | number | 0.0-1.0 in artifact fitness |
evidence_anchors | array[string] | Sources that informed generation |
safety_notes | array[string] | Security/reliability concerns |
next_actions | array[string] | Recommended follow-up steps |
Examples
Example 1: Generate API Endpoint
Input:
artifact_type: "code"
constraints:
- "TypeScript with strict mode"
- "Express.js middleware pattern"
- "Input validation with Zod"
- "Return 200 on success, 400 on validation error, 500 on server error"
context:
endpoint: "POST /api/users"
schema: "{ name: string, email: string, role: 'admin' | 'user' }"
format: "typescript"
Output:
artifact:
type: "code"
content: |
import { Request, Response, NextFunction } from 'express';
import { z } from 'zod';
const CreateUserSchema = z.object({
name: z.string().min(1).max(100),
email: z.string().email(),
role: z.enum(['admin', 'user'])
});
type CreateUserInput = z.infer<typeof CreateUserSchema>;
export async function createUser(
req: Request,
res: Response,
next: NextFunction
): Promise<void> {
try {
const parsed = CreateUserSchema.safeParse(req.body);
if (!parsed.success) {
res.status(400).json({
error: 'Validation failed',
details: parsed.error.issues
});
return;
}
const userData: CreateUserInput = parsed.data;
// TODO: Implement actual user creation logic
const user = await userService.create(userData);
res.status(200).json({ user });
} catch (error) {
next(error);
}
}
format: "typescript"
constraints_used:
- constraint: "TypeScript with strict mode"
satisfied: true
notes: "All types explicit, strict null checks compatible"
- constraint: "Express.js middleware pattern"
satisfied: true
notes: "Standard (req, res, next) signature with async handler"
- constraint: "Input validation with Zod"
satisfied: true
notes: "Zod schema with safeParse for type-safe validation"
- constraint: "HTTP status codes"
satisfied: true
notes: "200 success, 400 validation error, 500 via error middleware"
rationale: "Used safeParse over parse for explicit error handling without exceptions. Separated schema definition for reuse in tests. Left business logic as TODO for service layer."
alternatives_considered:
- "class-validator with decorators - rejected for runtime overhead"
- "Manual validation - rejected for type safety"
quality_signals:
- metric: "type_safety"
value: "high"
- metric: "error_handling"
value: "comprehensive"
- metric: "testability"
value: "high"
confidence: 0.9
evidence_anchors:
- "zod:docs:safeParse"
- "express:docs:error-handling"
assumptions:
- "userService exists and is imported"
- "Error middleware handles uncaught exceptions"
safety_notes:
- "Email validation does not prevent all injection attacks"
- "Consider rate limiting for user creation endpoint"
next_actions:
- "Implement userService.create method"
- "Add authentication middleware"
- "Write unit tests for validation logic"
Evidence pattern: Read existing codebase for patterns, reference library documentation for best practices.
Example 2: Generate Configuration Schema
Input:
artifact_type: "schema"
constraints:
- "JSON Schema draft-07"
- "Required fields: host, port, database"
- "Optional: ssl, poolSize, timeout"
- "Sensible defaults where appropriate"
context: "Database connection configuration"
format: "json"
Output:
artifact:
type: "schema"
content:
$schema: "http://json-schema.org/draft-07/schema#"
title: "DatabaseConfig"
type: "object"
required: ["host", "port", "database"]
properties:
host:
type: "string"
description: "Database server hostname"
examples: ["localhost", "db.example.com"]
port:
type: "integer"
minimum: 1
maximum: 65535
description: "Database server port"
default: 5432
database:
type: "string"
description: "Database name"
minLength: 1
ssl:
type: "boolean"
description: "Enable SSL/TLS connection"
default: true
poolSize:
type: "integer"
minimum: 1
maximum: 100
description: "Connection pool size"
default: 10
timeout:
type: "integer"
minimum: 0
description: "Connection timeout in milliseconds"
default: 30000
additionalProperties: false
format: "json"
constraints_used:
- constraint: "JSON Schema draft-07"
satisfied: true
notes: "$schema declaration included"
- constraint: "Required fields: host, port, database"
satisfied: true
notes: "Listed in required array"
- constraint: "Optional: ssl, poolSize, timeout"
satisfied: true
notes: "Not in required, have defaults"
- constraint: "Sensible defaults"
satisfied: true
notes: "port=5432 (PostgreSQL), ssl=true, poolSize=10, timeout=30s"
rationale: "Used PostgreSQL port as default assuming most common use case. SSL enabled by default for security. Pool size 10 balances resource usage with concurrency."
alternatives_considered:
- "MySQL port 3306 - rejected, PostgreSQL more common in modern stacks"
quality_signals:
- metric: "completeness"
value: "high"
- metric: "documentation"
value: "descriptions and examples included"
confidence: 0.95
evidence_anchors:
- "json-schema:draft-07:spec"
assumptions:
- "PostgreSQL is the target database"
- "Production deployment expects SSL"
safety_notes:
- "Consider encrypting credentials in actual config files"
- "poolSize limits should match database max_connections"
next_actions:
- "Add validation for connection string format"
- "Consider environment variable substitution"
Verification
- All required constraints marked as satisfied
- Artifact format matches requested format
- Quality signals include at least correctness and completeness
- Rationale explains key design decisions
- Safety notes address security considerations
Verification tools: Read (for pattern consistency), schema validators, linters
Safety Constraints
mutation: falserequires_checkpoint: falserequires_approval: falserisk: low
Capability-specific rules:
- Generated code must not contain hardcoded secrets or credentials
- Flag any security-sensitive patterns in generated content
- Do not generate content that violates constraints - report conflicts instead
- Include safety_notes for any security-relevant artifact
Composition Patterns
Commonly follows:
compare- Generate solution based on comparison winnerplan- Generate artifacts specified in planidentify- Generate based on identified requirementsdiscover- Generate to address discovered gaps
Commonly precedes:
verify- Generated artifacts should be verifiedact- Generated content may be written to filescritique- Generated plans should be critiqued
Anti-patterns:
- Never generate without constraints (unbounded generation)
- Avoid generate for retrieval (use
searchorretrieve) - Do not use generate for analysis (use
estimate,compare, etc.)
Workflow references:
- See
reference/composition_patterns.md#capability-gap-analysisfor generate-plan usage - See
reference/composition_patterns.md#debug-code-changefor code generation in fixes
Source
git clone https://github.com/synaptiai/agent-capability-standard/blob/main/skills/generate/SKILL.mdView on GitHub Overview
Generates new artifacts such as text, code, plans, or data that satisfy explicit constraints and serve a defined purpose. The process blends creative synthesis with strict requirements, delivering artifacts with rationale, quality signals, and safety notes.
How This Skill Works
Inputs specify artifact_type, constraints, and an optional context. The system clarifies requirements, gathers context, designs a generation plan, produces the artifact, then validates each constraint and documents the rationale and safety considerations before delivery.
When to Use It
- Generating code or scripts that must adhere to exact formatting, security, and performance constraints
- Creating a design plan or architecture document with strict structure and compatibility requirements
- Producing data, config, or schema artifacts that follow defined formats and schemas
- Requesting alternative designs to compare options and trade-offs
- Delivering artifacts with documented rationale, constraints traceability, and safety notes
Quick Start
- Step 1: Provide artifact_type constraints and optional context
- Step 2: The generator designs the approach and produces the artifact
- Step 3: Review constraint satisfaction, rationale, and safety notes; iterate if needed
Best Practices
- List must-have, should-have, and must-not-have constraints upfront
- Provide clear context and reference materials to guide generation
- Outline the artifact structure before generation and enforce it during production
- Validate each constraint and record any compromises or partial satisfactions
- Include a transparent rationale with trade-offs and known limitations
Example Use Cases
- A Python data processing script generated to meet a security and performance profile
- A JSON API contract produced with explicit field constraints and response shape
- A Markdown user guide written to a 600 word limit and a defined tone
- A SQL schema created with naming conventions and normalization rules
- A YAML deployment configuration that includes required fields and defaults