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/customise-workflow

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

From plugin
prd
59610 skills1 agent2 hooks1 MCP
Install
$ npx -y skills add anombyte93/prd-taskmaster --skill customise-workflow --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/customise-workflow

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

customise-workflow.SKILL.md
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`) r

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Ships withprd

prd-taskmaster by Atlas AI is an open-source engine for Claude Code that takes a one-line goal, interviews you like a senior PM, writes a **graded, placeholder-proof PRD, compiles it into a **dependency-ordered task graph, and executes every task with

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Python
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1mo ago
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10mo ago
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Repo: anombyte93/prd-taskmaster

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