swamp-model

name

swamp-model

description

Work with existing swamp models — structured automation units that define typed schemas, methods (validate, transform, enrich), and outputs for data processing. Use when searching for model types, describing schemas, creating inputs, running or executing methods, viewing outputs, or managing lifecycle (edit, delete). Do NOT use when the user wants to build, create, or implement a custom model type, Zod schema, or TypeScript model — that is swamp-extension. Do NOT use for orchestrating or chaining models in workflows — that is swamp-workflow. Triggers on "swamp model", "model type", "model schema", "create input", "type search", "type describe", "run method", "execute method", "validation method", "transform method", "enrichment model", "model validate", "model delete", "model edit", "model output", "output logs", "output format", "CEL expression".

Swamp Model Skill

Work with swamp models through the CLI.

Output Modes

• Execution (method run): Use default log output. Results are persisted in the datastore — use report get --json for structured detail (narrative, schema, pointers) or data get --json for specific resources.
• Retrieval (model get, data get, report get, output search): Use --json when you need structured data for action.
• Mutation (model create, model delete): Use --json to capture the structured result.

Prefer Direct Execution

For most use cases, direct type execution is the right approach — pass inputs at runtime without managing definition YAML files:

swamp model @<type> method run <method> <name> --input key=value

Inputs are automatically routed between global arguments and method arguments using the type's schemas. See references/direct-execution.md for details.

Use model create only when you need persistent, managed definitions — CEL expressions in global arguments, version-controlled definition files, or shared definitions referenced across multiple workflows.

Model Creation Rules (when using model create)

• Never generate model IDs — no uuidgen, crypto.randomUUID(), or manual UUIDs. Swamp assigns IDs automatically via swamp model create.
• Never write a model YAML file from scratch — always use swamp model create <type> <name> --json first, then edit the scaffold at the returned path, preserving the assigned id.
• Never modify the id field in an existing model file.
• Verify CLI syntax: If unsure about exact flags or subcommands, run swamp help model for the complete, up-to-date CLI schema.

Quick Reference

Task	Command
Search model types	swamp model type search [query] --json
Describe a type	swamp model type describe <type> --json
Create model input	swamp model create <type> <name> --json
Create with args	swamp model create <type> <name> --global-arg key=value --json
Search models	swamp model search [query] --json
Get model details	swamp model get <id_or_name> --json
Edit model input	swamp model edit [id_or_name]
Delete a model	swamp model delete <id_or_name> --json
Validate model	swamp model validate [id_or_name] --json
Validate by label	swamp model validate [id_or_name] --label policy --json
Validate by method	swamp model validate [id_or_name] --method create --json
Evaluate input(s)	swamp model evaluate [id_or_name] --json
Run a method	swamp model method run <id_or_name> <method>
Run with inputs	swamp model method run <name> <method> --input key=value
Run from stdin	echo '{"k":"v"}' | swamp model method run <name> <method> --stdin
Direct type exec	swamp model @<type> method run <method> <name> --input k=v
Skip all checks	swamp model method run <name> <method> --skip-checks
Skip check by name	swamp model method run <name> <method> --skip-check <n>
Skip check by label	swamp model method run <name> <method> --skip-check-label <l>
Search outputs	swamp model output search [query] --json
Get output details	swamp model output get <output_or_model> --json
View output logs	swamp model output logs <output_id> --json
View output data	swamp model output data <output_id> --json

Repository Structure

Model definitions are stored directly in the models/ directory, organized by type:

models/
  {normalized-type}/
    {model-id}.yaml

Internal data (evaluated definitions, data artifacts, outputs) lives in .swamp/:

.swamp/definitions-evaluated/{normalized-type}/{model-id}.yaml
.swamp/data/{normalized-type}/{model-id}/{data-name}/{version}/raw
.swamp/outputs/{normalized-type}/{model-id}/{output-id}.yaml

Search for Model Types

Find available model types in the system.

swamp model type search --json
swamp model type search "echo" --json

Output shape:

{
  "query": "",
  "results": [
    { "raw": "command/shell", "normalized": "command/shell" }
  ]
}

Describe Model Types

Get the full schema and available methods for a type.

swamp model type describe command/shell --json

Output shape:

{
  "type": { "raw": "command/shell", "normalized": "command/shell" },
  "version": "2026.02.09.1",
  "globalArguments": {/* JSON Schema */},
  "resourceAttributesSchema": {/* JSON Schema */},
  "methods": [
    {
      "name": "execute",
      "description": "Execute a shell command and capture output",
      "arguments": {/* JSON Schema */},
      "inputs": {/* JSON Schema (same as arguments) */}
    }
  ]
}

Key fields:

• globalArguments - JSON Schema for input YAML globalArguments section
• methods - Available operations with their per-method inputs schemas (also available as arguments for backward compatibility)

Create Model Inputs

swamp model create command/shell my-shell --json

Set globalArguments at creation time with --global-arg (repeatable):

swamp model create aws/ec2/vpc my-vpc \
  --global-arg region=us-east-1 \
  --global-arg cidrBlock=10.0.0.0/16 \
  --json

Use ${{ vault.get() }} expressions for secrets — never resolve a secret and pass the literal value (see swamp-vault skill for details):

swamp model create @user/my-api api-client \
  --global-arg 'apiKey=${{ vault.get(prod-secrets, API_KEY) }}' \
  --global-arg endpoint=https://api.example.com \
  --json

Dot notation creates nested objects:

--global-arg config.db.host=localhost --global-arg config.db.port=5432
# → globalArguments: { config: { db: { host: "localhost", port: "5432" } } }

Output shape:

{
  "path": "definitions/command/shell/my-shell.yaml",
  "type": "command/shell",
  "name": "my-shell"
}

After creation, edit the YAML file to set per-method arguments in the methods section.

Example input file:

id: 550e8400-e29b-41d4-a716-446655440000
name: my-shell
version: 1
tags: {}
methods:
  execute:
    arguments:
      run: "echo 'Hello, world!'"

Definition-Level Check Selection

Definitions can control which pre-flight checks run via the checks field:

id: 550e8400-e29b-41d4-a716-446655440000
name: my-vpc
version: 1
tags: {}
checks:
  require:
    - no-cidr-overlap # Must run, immune to --skip-checks CLI flags
  skip:
    - slow-api-check # Always skipped
globalArguments:
  cidrBlock: "10.0.0.0/16"
methods:
  create:
    arguments: {}

Precedence rules:

• skip always wins — even over require for the same check name
• require makes checks immune to --skip-checks, --skip-check <name>, and --skip-check-label <label> CLI flags (e.g., --skip-checks skips non-required checks but required checks still run)
• require checks still respect appliesTo method scoping
• model validate honors skip lists and warns on require/skip overlap; validation errors if a check name doesn't exist on the model type

Model Inputs Schema

Models can define an inputs schema for runtime parameterization:

id: 550e8400-e29b-41d4-a716-446655440000
name: my-deploy
version: 1
tags: {}
inputs:
  properties:
    environment:
      type: string
      enum: ["dev", "staging", "production"]
      description: Target environment
    dryRun:
      type: boolean
      default: false
  required: ["environment"]
globalArguments:
  target: ${{ inputs.environment }}
  simulate: ${{ inputs.dryRun }}
methods:
  deploy:
    arguments: {}

Inputs are provided at runtime with --input or --input-file and referenced in globalArguments using ${{ inputs.<name> }} expressions.

Factory pattern: Use inputs to create multiple instances from one model definition — for data reuse (same schema, different parameters) and concurrency (separate instances hold separate locks, so long-running methods on one instance don't block other instances). See references/scenarios.md#scenario-5.

Edit a Model

Recommended: Use swamp model get <name> --json to get the file path, then edit directly with the Edit tool, then validate with swamp model validate <name> --json.

Alternative methods:

• Interactive: swamp model edit my-shell (opens in system editor)
• Stdin: cat updated.yaml | swamp model edit my-shell --json

Delete a Model

Delete a model and all related artifacts (data, outputs, logs).

swamp model delete my-shell --json

Output shape:

{
  "deleted": true,
  "modelId": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
  "modelName": "my-shell",
  "artifactsDeleted": {
    "outputs": 5,
    "dataItems": 3
  }
}

Validate Model Inputs

Validate a model definition against its type schema. Use --label to run only checks with a specific label, and --method to simulate validation for a specific method context.

swamp model validate my-shell --json
swamp model validate --json                          # Validate all models
swamp model validate my-shell --label policy --json  # Only checks with label "policy"
swamp model validate my-shell --method create --json # Validate for a specific method

Output shape (single):

{
  "modelId": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
  "modelName": "my-shell",
  "type": "command/shell",
  "validations": [
    { "name": "Definition schema", "passed": true },
    { "name": "Global arguments", "passed": true },
    { "name": "Expression paths", "passed": true }
  ],
  "warnings": [
    {
      "name": "Environment variables detected",
      "message": "Data stored under this model will vary depending on these environment variables at runtime. Consider using separate models per environment, or vault.get() for sensitive values.",
      "envVars": [
        { "path": "globalArguments.baseUrl", "envVar": "JENKINS_BASE_URL" }
      ]
    }
  ],
  "passed": true
}

Output shape (all):

{
  "models": [
    { "modelId": "...", "modelName": "my-shell", "validations": [...], "warnings": [...], "passed": true }
  ],
  "totalPassed": 5,
  "totalFailed": 1,
  "totalWarnings": 1,
  "passed": false
}

IMPORTANT: Handling Validation Warnings

When warnings is non-empty, STOP and ask the user before proceeding. The most common warning is "Environment variables detected" — this means the model's behavior depends on env vars that may differ between machines or environments.

• If warnings contains env var usage, tell the user which fields use which env vars and ask if this is intentional.
• Suggest alternatives: separate models per environment (e.g., prod-jenkins and dev-jenkins with hardcoded values), or vault.get() for sensitive values.
• Never silently run a method on a model with env var warnings without user confirmation — the data artifacts will be stored under the model name and may contain results from an unintended environment.

Expression Language

Model inputs support CEL expressions using ${{ <expression> }} syntax. Reference types, data versioning functions, and examples are in references/expressions.md.

Evaluate Model Inputs

Evaluate expressions and write results to inputs-evaluated/.

swamp model evaluate my-subnet --json
swamp model evaluate --all --json

Output shape:

{
  "evaluatedInputs": [
    {
      "name": "my-subnet",
      "type": "aws/subnet",
      "path": "inputs-evaluated/aws/subnet/my-subnet.yaml"
    }
  ]
}

Run Methods

Execute a method on a model input.

swamp model method run my-shell execute
swamp model method run my-deploy create --input environment=prod
swamp model method run my-deploy create --input environment=prod --input replicas=3
swamp model method run my-deploy create --input config.timeout=30  # dot notation for nesting
swamp model method run my-deploy create --input 'tags:json=["prod","west"]'  # :json suffix for arrays/objects
swamp model method run my-deploy create --input '{"environment": "prod"}'  # legacy single-shot JSON
swamp model method run my-deploy create --input-file inputs.yaml
echo '{"environment": "prod"}' | swamp model method run my-deploy create --stdin
printf '{"environment":"dev"}\n{"environment":"prod"}' | swamp model method run my-deploy create --stdin  # NDJSON: one run per line
swamp data query 'modelName == "source"' --json | jq -c '.results[] | {environment: .attributes.env}' | swamp model method run my-deploy create --stdin
swamp model method run my-deploy create --last-evaluated
swamp model method run my-deploy create --skip-checks
swamp model method run my-deploy create --skip-check valid-region
swamp model method run my-deploy create --skip-check-label live

Data versioning: Running a method multiple times creates new data versions (v1, v2, ...), never overwrites. Each run's artifacts are preserved. Use swamp data get <name> <spec> --version <N> to access a specific version, or see the swamp-data skill for version history and cleanup.

Pre-flight checks run automatically before mutating methods (create, update, delete, action). Read-only methods (sync, get, etc.) do not trigger checks.

Environment variable warnings are emitted before execution if the model definition uses ${{ env.* }} expressions. When you see these warnings in the output, pause and confirm with the user that the current environment variables are correct for the intended target. See Handling Validation Warnings above.

Options:

Flag	Description
--input <value>	Input values (key=value repeatable, or JSON)
--input-file <f>	Input values from YAML file
--last-evaluated	Use previously evaluated model (skip eval)
--skip-checks	Skip all pre-flight checks
--skip-check <name>	Skip a specific check by name (repeatable)
--skip-check-label <label>	Skip all checks with a given label (repeatable)
--driver <driver>	Override execution driver (e.g. raw, docker)

Output shape:

{
  "outputId": "d1e2f3a4-b5c6-4d7e-f8a9-b0c1d2e3f4a5",
  "modelId": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
  "modelName": "my-shell",
  "method": "execute",
  "status": "succeeded",
  "duration": 150,
  "artifacts": {
    "resource": ".swamp/data/command/shell/a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d/result/1/raw"
  }
}

Model Outputs

Use swamp model output search, output get, output logs, and output data to inspect method execution results. See references/outputs.md for commands and output shapes.

Inspecting Factory Method Results

When a factory method produces multiple data artifacts, use swamp data query (from the swamp-data skill) to explore them:

# List all artifacts from a model
swamp data query 'modelName == "my-scanner"' --select '{"name": name, "version": version}'

# Filter by spec name and extract fields
swamp data query 'modelName == "my-scanner" && specName == "result"' \
    --select '{"host": attributes.hostname, "status": attributes.status}'

For single-output inspection, swamp model output get <name> --json is sufficient. For browsing N artifacts, data query with --select is the primary pattern.

Workflow Example

Design check — before creating a model, ask the user:

1. Will this model have multiple methods that might run at the same time (e.g., a long-running build AND an SSH session)?
2. Will any method be long-running (builds, deployments, large data transfers)?

If yes to either: each independent concern should be a separate model instance. Swamp holds an exclusive per-model lock for the entire duration of a method execution — a 20-minute build locks out every other method on that model. Use the factory pattern to split concerns into separate instances so they can run concurrently. See references/scenarios.md#scenario-5.

Steps:

1. Search community extensions first: swamp extension search <query> --json — prefer @swamp/* official extensions. If a match exists, install with swamp extension pull <package> instead of building from scratch
2. Search local types: swamp model type search "shell" --json
3. Describe to understand the schema: swamp model type describe command/shell --json
4. Create an input file: swamp model create command/shell my-shell --json
5. Edit the YAML file to set methods.execute.arguments.run
6. Validate the model: swamp model validate my-shell --json
7. Check warnings — if the validation output has non-empty warnings, stop and ask the user before proceeding (see Handling Validation Warnings)
8. Run the method: swamp model method run my-shell execute
9. View the output: swamp model output get my-shell --json

Data Ownership

Data is owned by the creating model — see references/data-ownership.md for rules and validation.

Choosing the Right Approach

Task	Approach
New API/service integration	Extension model (swamp-extension)
Existing model missing a method	Extend it (swamp-extension)
Reusable data pipeline (reports, analysis, summaries)	Report extension (swamp-report)
Ad-hoc debugging or one-off data inspection	Inline processing is fine

When to Use Other Skills

Need	Use Skill
Create/run workflows	swamp-workflow
Manage secrets	swamp-vault
Repository structure	swamp-repo
Manage data lifecycle	swamp-data
Explore/query method results	swamp-data
Create custom TypeScript models	swamp-extension
Create reports for models	swamp-report
Understand swamp internals	swamp-troubleshooting

References

• Outputs: See references/outputs.md for output search, get, logs, and data commands with output shapes
• Examples: See references/examples.md for complete model workflows and CEL expression reference
• Troubleshooting: See references/troubleshooting.md for common errors and fixes
• Scenarios: See references/scenarios.md for end-to-end scenarios (shell commands, chained lookups, runtime inputs)
• Data chaining: See references/data-chaining.md for command/shell model examples and chaining patterns
• Execution drivers: See references/execution-drivers.md