Customise Workflow Agent Skill

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:

• This skill NEVER writes into .atlas-ai/customizations/ — that's user

territory.

• Phase skills read .atlas-ai/customizations/ as a fallback *after* the

curated atlas.json but *before* documented defaults.

• 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

• Never ask the user to edit JSON directly — the skill asks curated questions

and writes the file.

• Questions are curated and AI-adapted, not a fixed form — adapt follow-ups

to earlier answers.

• Every answer is validated before being written (validate-answer).
• Config is idempotent — re-running updates cleanly.
• Config is per-project (lives in .atlas-ai/config/), not global.
• Customization files live in .atlas-ai/customizations/ and are

user-authored — this skill never overwrites them.

• Phase skills must GRACEFULLY FALL BACK to documented defaults when config

keys are missing.