From prd
Guides users through curated questions to customise prd-taskmaster plugin preferences (provider, validation, execution mode, etc.) and writes config to .atlas-ai/config/atlas.json.
How this skill is triggered — by the user, by Claude, or both
Slash command
/prd:customise-workflowThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
AI-driven workflow customisation for the `prd-taskmaster` plugin.
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/)
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 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.
LOAD → ASK → VALIDATE → WRITE → VERIFY
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.
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.)
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.
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.
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.
| 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) |
.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.
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:
.atlas-ai/customizations/ — that's user
territory..atlas-ai/customizations/ as a fallback after the
curated atlas.json but before documented defaults.validate-answer)..atlas-ai/config/), not global..atlas-ai/customizations/ and are
user-authored — this skill never overwrites them.npx claudepluginhub anombyte93/prd-taskmaster --plugin prdConfigures Plan-Build-Run settings: workflow depth, model profiles, features, git branching/mode, and gates. Interactive or direct args like 'depth standard' or 'feature auto_continue on'.
Views, creates, and edits Accelerator plugin configuration. Manages shared team config and personal overrides for project context and per-skill customizations.
Phase 3 of the prd-taskmaster pipeline: detects installed capabilities, recommends one execution mode (A/B/C/D), appends task workflow to CLAUDE.md, and dispatches the chosen mode via AskUserQuestion.