mthds-build

Build AI Method using the MTHDS standard

Create new MTHDS bundles through an adaptive, phase-based approach. This skill guides you through drafting (markdown), structuring (CLI/JSON), and assembling complete .mthds bundles.

Philosophy

1. Drafting phases: Generate human-readable markdown documents
2. Structuring phases: Use agent CLI commands for JSON-to-TOML conversion
3. Flow overviews: Summarize flow structure at each phase
4. Iterative: Refine at each phase before proceeding

Mode Selection

How mode is determined

1. Explicit override: If the user states a preference, always honor it:

   • Automatic signals: "just do it", "go ahead", "automatic", "quick", "don't ask"
   • Interactive signals: "walk me through", "help me", "guide me", "step by step", "let me decide"

2. Skill default: Each skill defines its own default based on the nature of the task.

3. Request analysis: If no explicit signal and no strong skill default, assess the request:

   • Detailed, specific requirements → automatic
   • Brief, ambiguous, or subjective → interactive

Mode behavior

Automatic mode:

• State assumptions briefly before proceeding
• Make reasonable decisions at each step
• Present the result when done
• Pause only if a critical ambiguity could lead to wasted work

Interactive mode:

• Ask clarifying questions at the start
• Present options at decision points
• Confirm before proceeding at checkpoints
• Allow the user to steer direction

Mode switching

• If in automatic mode and the user asks a question or gives feedback → switch to interactive for the current phase
• If in interactive mode and the user says "looks good, go ahead" or similar → switch to automatic for remaining phases

Default: Automatic for simple-to-moderate methods. Interactive for complex multi-step methods or when the user's request is ambiguous.

Detection heuristics:

• User provides a clear one-sentence goal → automatic
• User describes a complex multi-step process → interactive
• User mentions batching, conditions, or parallel execution → interactive
• User says "create a pipeline for X" with no elaboration → automatic

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Step 0 — Environment Check (mandatory, do this FIRST)

Run this command to check toolchain status:

~/.claude/plugins/marketplaces/mthds-plugins/bin/mthds-env-check "0.3.4" 2>/dev/null || ../mthds-plugins/bin/mthds-env-check "0.3.4" 2>/dev/null || echo "MTHDS_ENV_CHECK_MISSING"

Interpret the output:

• MTHDS_AGENT_MISSING → STOP. Do not proceed. Tell the user:

The mthds-agent CLI is required but not installed. Install it with:

npm install -g mthds

Then re-run this skill.

• MTHDS_AGENT_VERSION_UNKNOWN → STOP. The installed mthds-agent returned an unparseable version. Tell the user:

Could not parse the output of mthds-agent --version. Your installation may be corrupt. Reinstall with:

npm install -g mthds@latest

Then re-run this skill.

• MTHDS_AGENT_OUTDATED <installed> <required> → The installed mthds-agent is too old for this plugin. Do not hard-stop. Instead, tell the user their mthds-agent (v<installed>) is older than the required v<required>, then follow the upgrade flow to offer upgrading mthds-agent via npm install -g mthds@latest. After the upgrade flow completes (whether the user upgraded or declined), proceed to Step 1. The upgrade flow's "Not now" and "Never ask" options let users continue with current versions.

• MTHDS_UPDATE_CHECK_FAILED ... → WARN. The update check command failed. Show the error output to the user. Suggest checking network connectivity and mthds-agent installation. Proceed to Step 1 with current versions.

• UPGRADE_AVAILABLE ... → Read upgrade flow and follow the upgrade prompts before continuing to Step 1.

• JUST_UPGRADED ... → Announce what was upgraded to the user, then continue to Step 1.

• MTHDS_ENV_CHECK_MISSING → WARN. The env-check script was not found at either expected path. Tell the user the environment check could not run, but proceed to Step 1.

• No output or UP_TO_DATE → Proceed to Step 1.

• Any other output → WARN. The preamble produced unexpected output. Show it to the user verbatim. Proceed to Step 1 cautiously.

Do not write .mthds files manually, do not do any other work. The CLI is required for validation, formatting, and execution — without it the output will be broken.

No backend setup needed: This skill works without configuring inference backends or API keys. You can start building/validating methods right away. Backend configuration is only needed to run methods with live inference — use /mthds-runner-setup when you're ready.

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Phase 1: Understand Requirements

Goal: Gather complete information before planning.

Ask the user:

• What are the method's inputs? (documents, images, text, structured data)
• What outputs should it produce?
• What transformations are needed?
• Are there conditional branches or parallel operations?
• Should items be processed in batches?

Output: Requirements summary (keep in context)

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Phase 2: Draft the Plan

Goal: Create a pseudo-code narrative of the method.

Draft a plan in markdown that describes:

• The overall flow from inputs to outputs
• Each processing step with its purpose
• Variable names (snake_case) for inputs and outputs of each step
• Where structured data or lists are involved

Rules:

• Name variables consistently across steps
• Use plural names for lists (e.g., documents), singular for items (e.g., document)
• Don't detail types yet - focus on the flow

Show bundle overview — see Build Phases for the overview description.

Output: Plan draft (markdown)

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Phase 3: Draft Concepts

Goal: Identify all data types needed in the method.

From the plan, identify input, intermediate, and output concepts.

For each concept, draft:

• Name: PascalCase, singular noun (e.g., Invoice not Invoices)
• Description: What it represents
• Type: Either refines: NativeConcept OR structure: {...}

Native concepts (built-in, do NOT redefine): Text, Html, Image, Document, Number, Page, TextAndImages, ImgGenPrompt, JSON, SearchResult, Anything, Dynamic. See MTHDS Language Reference — Native Concepts

Note: Document is the native concept for any document (PDF, Word, etc.). Image is for any image format (JPEG, PNG, etc.). File formats like "PDF" or "JPEG" are not concepts.

Each native concept has accessible attributes (e.g., Image has url, public_url, filename, caption...; Document has url, public_url, filename...; Page has text_and_images and page_view). See Native Content Types for the full attribute reference — essential for $var.field prompts and construct blocks.

Concept naming rules:

• No adjectives: Article not LongArticle
• No circumstances: Argument not CounterArgument
• Always singular: Employee not Employees

Output: Concepts draft (markdown)

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Phase 4: Structure Concepts

Goal: Convert concept drafts to validated TOML using the CLI.

Prepare JSON specs for all concepts, then convert them in parallel by making multiple concurrent tool calls. Each command outputs validated TOML directly — keep the output in context for assembly in Phase 8.

Example (3 concepts converted in parallel):

# Call all three in parallel (single response, multiple tool calls):
mthds-agent concept --spec '{"concept_code": "Invoice", "description": "A commercial invoice document", "structure": {"invoice_number": "The unique identifier", "vendor_name": {"type": "text", "description": "Vendor name", "required": true}, "total_amount": {"type": "number", "description": "Total amount", "required": true}}}'
mthds-agent concept --spec '{"concept_code": "LineItem", "description": "A single line item on an invoice", "structure": {"description": "Item description", "quantity": {"type": "integer", "required": true}, "unit_price": {"type": "number", "required": true}}}'
mthds-agent concept --spec '{"concept_code": "Summary", "description": "A text summary of content", "refines": "Text"}'

Field types: text, integer, boolean, number, date, concept, list

Choices (enum-like constrained values):

status = {choices = ["pending", "processing", "completed"], description = "Order status", required = true}
score = {type = "number", choices = ["0", "0.5", "1", "1.5", "2"], description = "Score on a half-point scale"}

When choices is present, type defaults to text if omitted. You can also pair choices with integer or number types explicitly.

Nested concept references in structures:

field = {type = "concept", concept_ref = "my_domain.OtherConcept", description = "...", required = true}
items = {type = "list", item_type = "concept", item_concept_ref = "my_domain.OtherConcept", description = "..."}

Output: Validated concept TOML (raw TOML output, held in context for Phase 8)

Partial failures: If some commands fail, fix the failing specs using the error message (error_domain: "input" means the spec is wrong). Re-run only the failed commands.

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Phase 5: Draft the Flow

Goal: Design the complete pipeline structure with controller selection.

Controller Selection Guide

Controller	Use When	Key Pattern
PipeSequence	Steps must execute in order	step1 → step2 → step3
PipeBatch	Same operation on each list item	map(items, transform)
PipeParallel	Independent operations run together	fork → join
PipeCondition	Route based on data values	if-then-else

Operator Selection Guide

Operator	Use When
PipeLLM	Generate text or structured objects with AI
PipeExtract	Extract content from PDF/Image/Web Page → Page[]
PipeCompose	Template text or construct objects
PipeImgGen	Generate images from text prompts
PipeSearch	Search the web for information → SearchResult
PipeFunc	Custom Python logic

Critical — PipeCondition requires a default_outcome field: The default_outcome field is required for PipeCondition, even when the outcomes appear exhaustive (e.g., a boolean-like "yes"/"no" split). Set it to "continue" to pass the output through unchanged, or to one of the outcome pipes as a safe default.

Critical — PipeImgGen requires a prompt field: The prompt field is required for PipeImgGen. It is a template that defines the text sent to the image generation model — use $variable syntax to insert inputs. Examples:

• Direct passthrough: prompt = "$img_prompt" — uses the input as-is
• Template with context: prompt = "A black and white sketch of $description" — wraps the input in a richer prompt

Even if the input already contains the full prompt text, you must still declare the prompt field. Without it, validation fails with missing required fields: 'prompt'.

Note: Page[] outputs from PipeExtract automatically convert to text when inserted into prompts using @variable.

Show detailed flow — see Build Phases for all controller flow patterns.

Output: Flow draft with pipe contracts (markdown)

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Phase 6: Review & Refine

Goal: Validate consistency before structuring.

Check:

• Main pipe is clearly identified and handles method inputs
• Variable names are consistent across all pipes
• Input/output types match between connected pipes
• PipeSequence steps: each step has result (required) — no inputs field on steps (data flows through working memory)
• PipeSequence batch steps: batch_as (singular) differs from batch_over (plural)
• PipeSequence batch steps: batch_over supports dotted paths for nested attributes (e.g., "search_result.sources" to iterate over sources inside a SearchResult)
• PipeBatch branches receive singular items, not lists
• PipeBatch: input_item_name (singular) differs from input_list_name (plural) and all inputs keys
• PipeCondition has a default_outcome — required even when outcomes seem exhaustive
• PipeImgGen has a prompt field (template that references inputs, e.g., prompt = "$description" or prompt = "A watercolor painting of $subject") — required even when the input IS the prompt
• PipeImgGen inputs are text-compatible (add PipeLLM if needed to craft the prompt first)
• No circular dependencies

Confirm with user before proceeding to structuring.

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Phase 7: Structure Pipes

Goal: Convert pipe drafts to validated TOML using the CLI.

Omit model by default — a default model is used automatically and handles most cases. Only set model when the pipe clearly needs a specialized model (e.g., vision, code analysis, high-quality image generation) or when the user explicitly requests a specific model. See Model References for the reference kinds and decision guide.

To look up available models when needed:

# get list of presets and model aliases:
mthds-agent models --type llm
mthds-agent models --type extract
mthds-agent models --type img_gen
mthds-agent models --type search

If the user asks for a specific model but the request is ambiguous, use check-model to resolve it:

mthds-agent check-model "<user's request>" --type llm

Prepare JSON specs for all pipes, then convert them in parallel by making multiple concurrent tool calls.

Required --spec JSON fields: type, pipe_code, description (short phrase), and optionally model for PipeLLM, PipeExtract, PipeImgGen, PipeSearch.

PipeImgGen prompt is required: The prompt field must be included in the --spec JSON. It is a template — use $variable to insert inputs. Examples: "prompt": "$img_prompt" (passthrough) or "prompt": "A black and white sketch of $description" (template with context). Omitting prompt causes a validation error.

For detailed CLI examples for each pipe type (PipeLLM, PipeSequence, PipeBatch, PipeCondition, PipeCompose, PipeParallel, PipeExtract, PipeImgGen, PipeSearch), see Build Phases.

Output: Validated pipe TOML (raw TOML output, held in context for Phase 8)

Partial failures: Fix failing specs using the error message. Re-run only the failed commands.

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Phase 8: Assemble Bundle

Goal: Combine all parts into a complete .mthds file.

Save location: Always save method bundles to mthds-wip/. Do not ask the user for the save location.

Procedure:

1. Create the output directory: mkdir -p mthds-wip/<bundle_dir>/
2. Compose the .mthds file by combining the CLI-validated TOML fragments from Phases 4 and 7 (this is deterministic assembly, not manual authoring), using this structure:

domain = "<domain>"
description = "<description>"
main_pipe = "<main_pipe_code>"

# Concept TOML from Phase 4
[concept.MyInput]
# ...

# Pipe TOML from Phase 7
[pipe.main_pipe_code]
# ...

3. Write the file using the Write tool to mthds-wip/<bundle_dir>/bundle.mthds — this triggers the PostToolUse hook for automatic lint/format/validate.

No intermediate files are needed. The concept --spec and pipe --spec commands (Phases 4 and 7) already validated each fragment — assembly is just combining them with the bundle header.

For the full .mthds file structure, see Build Phases.

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Phase 9: Validate & Test

Goal: Ensure the bundle is valid and works correctly.

Always use -L pointing to the bundle's own directory to avoid namespace collisions with other bundles in the project.

# Validate and generate flowchart (isolated from other bundles)
mthds-agent validate bundle mthds-wip/pipeline_01/bundle.mthds -L mthds-wip/pipeline_01/ --graph

# Generate example inputs
mthds-agent inputs bundle mthds-wip/pipeline_01/bundle.mthds -L mthds-wip/pipeline_01/

On success, dry_run.html is saved next to the bundle. The JSON output includes the path in graph_files.

Fix any validation errors and re-validate.

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Phase 10: Deliver

Goal: Generate input template after a successful build.

After validation passes (Phase 9), generate the input template:

# Input template (extracts the input schema as JSON)
mthds-agent inputs bundle <mthds_file> -L <output_dir>/

Replace <mthds_file> and <output_dir> with actual paths from the build output.

Present Results

After the command succeeds:

1. Input schema: Show the inputs JSON from the command output so the user can see what the method expects. Do NOT save it to inputs.json — input preparation is handled exclusively by /mthds-inputs.

2. Flowchart: Tell the user that an interactive flowchart (dry_run.html) was generated during validation next to the bundle.

3. Next steps — test with mock inference: Suggest a dry run to verify the method structure works:

   To test this method with mock inference (no real inputs needed):

   mthds-agent run bundle <output_dir>/ --dry-run --mock-inputs
   

4. Next steps — prepare inputs and run:

   To prepare inputs for a real run, use /mthds-inputs. It can generate a placeholder template, create synthetic test data, or integrate your own files. Then:

   mthds-agent run bundle <output_dir>/
   

   Replace <output_dir> with the actual output directory path used throughout the build.

NEVER write inputs.json manually. If the user provides files, paths, or asks to run with real data, you MUST invoke /mthds-inputs — it handles path resolution (paths must be relative to inputs.json, not CWD), placeholder formatting, file copying, and multiple input strategies. Writing inputs.json by hand bypasses all of this and produces broken paths.

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Quick Reference

Multiplicity Notation

• Text - single item
• Text[] - variable-length list
• Text[3] - exactly 3 items

Prompt Variables

• @variable - Block insertion (multi-line, with delimiters)
• $variable - Inline insertion (short text)
• @?variable - Conditional block insertion (only renders if variable is truthy)
• $var.field - Access nested field (dotted paths work with all three patterns)
• Raw Jinja2 {{ }} / {% %} also supported
• These work in PipeLLM, PipeImgGen, PipeSearch, and PipeCompose templates

Naming Conventions

• Domain: snake_case
• Concepts: PascalCase, singular
• Pipes: snake_case
• Variables: snake_case

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Reference

• Error Handling — read when CLI returns an error to determine recovery
• MTHDS Agent Guide — read for CLI command syntax or output format details
• MTHDS Language Reference — read when writing or modifying .mthds TOML syntax
• Native Content Types — read when using $var.field in prompts or from in construct blocks, to know which attributes each native concept exposes
• Build Phases — read for detailed flow descriptions and CLI examples per phase
• Model References — read when choosing whether and how to set a model on a pipe