/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
$ npx -y skills add anombyte93/prd-taskmaster --skill customise-workflow --agent claude-codeHow 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.
- 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.mdname: 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
Read more
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
Showing the first part of this file.
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
Other skills on prd.
- /atlas
The Atlas engine — turn any goal into a validated PRD and an executable, verified task graph. Brand-name entrypoint; a thin alias for the `go` orchestrator. Use when the user types /prd:atlas, says "I want to build", or asks for a PRD / task-driven build.
Open skill - /discover
Phase 1 of the prd-taskmaster pipeline: brainstorm-driven discovery. Delegates to superpowers:brainstorming in Interactive Mode (one adaptive question at a time), or self-brainstorms in Autonomous Mode when no user is present. Intercepts before the brainstorming chain hands off
Open skill - /execute-fleet
Phase execution skill for licensed Atlas Fleet runs. Use when HANDOFF has selected Atlas Fleet and the project should be executed across isolated launcher worktrees with inbox-based result collection, verified CDD cards, sequential integration merges, and one final PR.
Open skill - /execute-task
Execute the next TaskMaster task using the implementation plan with CDD verification. Picks the next ready task, matches it to the plan step, implements via a dispatched subagent, verifies subtasks with evidence, marks the task done, and loops until every task is complete. Wraps
Open skill - /expand-tasks
Expand all TaskMaster tasks with deep research before coding begins. Reads tasks.json, launches parallel research agents per task in waves using the research-expander agent. Writes findings back to tasks.json. Part of the prd-taskmaster toolkit. Use after PRD is parsed and
Open skill - /generate
Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholders_found, grade thresholds), parses the PRD into tasks via task-master, runs
Open skill

