customise-workflow

name	customise-workflow
description	Customise the prd-taskmaster plugin workflow via curated brainstorm questions. The AI asks, the user answers in plain English, and the skill writes their preferences to .atlas-ai/config/atlas.json. Future runs of prd-taskmaster read that file and apply user preferences to phase gates, validation strictness, default provider, preferred execution mode, and template choice. For deeper tweaks beyond the curated questions, users can hand-edit files in .atlas-ai/customizations/. Use when the user says "customise workflow", "customize workflow", "adjust my PRD settings", "tune the skill", or wants to change how prd-taskmaster behaves.
user-invocable	true
allowed-tools	["Read","Write","Edit","Bash","AskUserQuestion","ToolSearch","mcp__atlas-engine","mcp__plugin_prd_go","mcp__plugin_prd-taskmaster_go","mcp__plugin_atlas-go_go"]

customise-workflow

AI-driven workflow customisation for the prd-taskmaster plugin. Replaces manual JSON editing. Part of the plugin's companion-skills family.

Script: skills/customise-workflow/script.py (all commands output JSON) Plugin config root: .atlas-ai/ (per-project, lives alongside TaskMaster's .taskmaster/)

When to Use

Activate when the user says: "customise workflow", "customize workflow", "adjust PRD settings", "tune the skill", "change my defaults", or "personalise prd-taskmaster".

Skip: generating a new PRD (use /prd:go), executing tasks (use HANDOFF modes), or running research expansion (use /expand-tasks).

The One Rule

The AI asks the questions and writes the config. The user never manually edits JSON. The config file is the output, not the input. If the user wants tweaks beyond the curated questions, point them at .atlas-ai/customizations/ (see "Customizations directory" below) — do not hand them raw JSON.

Flow

LOAD → ASK → VALIDATE → WRITE → VERIFY

Phase 1: LOAD current config

Run the script to load existing preferences (or defaults if first run):

python3 skills/customise-workflow/script.py load-config

Returns JSON with current preferences across 6 categories: provider, validation, execution, template, autonomous, gates. Writes to .atlas-ai/config/atlas.json if missing, seeding defaults.

Phase 2: ASK curated questions

Read questions/curated-questions.md and ask each one via AskUserQuestion. The questions are curated so plain-English answers map cleanly to config keys. Example:

Q1: Which AI provider do you prefer for task generation?
  Options: Gemini (free, token-efficient), Claude Code (free, Max only),
           OpenAI GPT-4, Anthropic Direct API, OpenRouter, Ollama (local)

Q2: How strict should PRD validation be?
  Options: Strict (block on NEEDS_WORK), Normal (warn but allow GOOD+),
           Lenient (accept ACCEPTABLE+)

Q3: Which execution mode should prd-taskmaster default to?
  Options: A (Plan Mode), B (Ralph loop), C (Atlas Fleet), ...

...

Do NOT ask all questions at once. Ask one curated question at a time and adapt follow-ups based on answers. (Same pattern as superpowers:brainstorming.)

Phase 3: VALIDATE answers

Run the script with each user answer as it arrives. The script validates the answer against allowed values and returns either ok: true or a hint about what's wrong.

python3 skills/customise-workflow/script.py validate-answer \
  --key provider_main --value gemini-cli

If validation fails, re-ask the question with the hint. Never write an invalid value.

Phase 4: WRITE config

After all curated questions are answered, commit the config:

python3 skills/customise-workflow/script.py write-config --input /tmp/answers.json

This writes to .atlas-ai/config/atlas.json in the current project. Idempotent — re-running customise-workflow reads and updates the existing file. The script creates the .atlas-ai/config/ directory if missing.

Phase 5: VERIFY

Show the user their final config and confirm it matches their intent:

python3 skills/customise-workflow/script.py show-config

If the user says "that's not what I meant" for any key, re-enter Phase 2 for just that key, re-validate, and re-write.

Script Commands Reference

Command	Purpose
load-config	Load current .atlas-ai/config/atlas.json (or defaults)
list-questions	Return the curated question set as JSON
validate-answer --key K --value V	Validate a single answer
write-config --input <file>	Write validated answers to .atlas-ai/config/atlas.json
show-config	Display current config
reset-config	Delete .atlas-ai/config/atlas.json (back to defaults)

Config Schema

.atlas-ai/config/atlas.json has 7 top-level keys:

{
  "token_economy": "conservative|balanced|performance",
  "provider": {
    "main": "gemini-cli|claude-code|anthropic|openai|openrouter|ollama|...",
    "model_main": "gemini-3-pro-preview|sonnet|gpt-4o|...",
    "research": "gemini-cli|perplexity|...",
    "model_research": "sonar-pro|gemini-3-pro-preview|...",
    "fallback": "gemini-cli|claude-code|...",
    "model_fallback": "gemini-3-flash-preview|haiku|..."
  },
  "validation": {
    "strictness": "strict|normal|lenient",
    "ai_review_default": true,
    "min_passing_grade": "EXCELLENT|GOOD|ACCEPTABLE|NEEDS_WORK"
  },
  "execution": {
    "preferred_mode": "A|B|C|D|E|F|G|H|I|J",
    "auto_handoff": true,
    "external_tool": "cursor|codex-cli|gemini-cli|..."
  },
  "template": {
    "default": "comprehensive|minimal",
    "custom_template_path": null
  },
  "autonomous": {
    "allow_self_brainstorm": true,
    "ralph_loop_auto_approve": true
  },
  "gates": {
    "skip_phase_0_if_validated": false,
    "skip_user_approval_in_discovery": false,
    "require_research_expansion": true
  }
}

Phase files (skills/setup, skills/discover, skills/generate, skills/handoff, skills/execute-task) read this config at runtime and apply user preferences before falling back to documented defaults.

token_economy here is honored by the engine itself: load_fleet_config reads it from this file when .atlas-ai/fleet.json does not set one (fleet.json wins if it does), so the economy you pick via this skill actually drives model-tier routing.

Customizations directory

For tweaks that go beyond the curated questions — custom template overrides, provider-model mapping tables, gate hooks, per-phase overrides — users can drop files into .atlas-ai/customizations/. This is the escape hatch for power users. The curated questions cover the 80% case; the customization directory covers everything else.

Expected layout:

.atlas-ai/
  config/
    atlas.json              # written by this skill
  customizations/           # user-editable, never overwritten by this skill
    templates/              # custom PRD templates
    prompts/                # provider prompt overrides
    gates/                  # custom gate predicates
    README.md               # user-authored notes

Rules:

1. This skill NEVER writes into .atlas-ai/customizations/ — that's user territory.
2. Phase skills read .atlas-ai/customizations/ as a fallback after the curated atlas.json but before documented defaults.
3. When a user asks for a setting not covered by curated questions, the AI proposes a customization file shape, the user edits, and the AI verifies the file parses.

Critical Rules

1. Never ask the user to edit JSON directly — the skill asks curated questions and writes the file.
2. Questions are curated and AI-adapted, not a fixed form — adapt follow-ups to earlier answers.
3. Every answer is validated before being written (validate-answer).
4. Config is idempotent — re-running updates cleanly.
5. Config is per-project (lives in .atlas-ai/config/), not global.
6. Customization files live in .atlas-ai/customizations/ and are user-authored — this skill never overwrites them.
7. Phase skills must GRACEFULLY FALL BACK to documented defaults when config keys are missing.