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Phantom

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GadaaLabs
phantom

Use when executing implementation plans with independent tasks in the current session

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill namephantom
Stars
67
Forks
10
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GadaaLabs on GitHub. Read the source before you install it.

Installation

Install the Phantom AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/phantom .claude/skills/phantom
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Phantom in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Phantom on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Phantom is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Subagent-Driven Development

PHANTOMA phantom operates in the shadows — independently, without disturbing the main operation. When invoked: executes each implementation task through a fresh subagent with zero session context, then runs two-stage review (spec compliance → code quality) before accepting the result.

Execute plan by dispatching fresh subagent per task, with two-stage review after each: spec compliance review first, then code quality review.

Why subagents: You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.

Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration

When to Use

ConditionUse
Have plan + independent tasks + stay in sessionPHANTOM
Have plan + want parallel fresh sessionEXODUS
No plan yetDesign first (ARCHITECT → BLUEPRINT)
Tasks tightly coupledManual execution

vs. Executing Plans (parallel session):

  • Same session (no context switch)
  • Fresh subagent per task (no context pollution)
  • Two-stage review after each task: spec compliance first, then code quality
  • Faster iteration (no human-in-loop between tasks)

The Process

Setup (once): Read plan → extract all tasks with full text → create TodoWrite

Per task loop:

  1. Dispatch implementer subagent with full task text + context
  2. Answer any questions before implementation begins
  3. Implementer implements, tests, self-reviews, commits → reports status
  4. Dispatch spec compliance reviewer → loop until ✅
  5. Dispatch code quality reviewer → loop until ✅
  6. Mark task complete in TodoWrite → next task

After all tasks: Dispatch final code reviewer → use superpowers:seal

Model Selection

Use the least powerful model that can handle each role to conserve cost and increase speed.

Mechanical implementation tasks (isolated functions, clear specs, 1-2 files): use a fast, cheap model. Most implementation tasks are mechanical when the plan is well-specified.

Integration and judgment tasks (multi-file coordination, pattern matching, debugging): use a standard model.

Architecture, design, and review tasks: use the most capable available model.

Task complexity signals:

  • Touches 1-2 files with a complete spec → cheap model
  • Touches multiple files with integration concerns → standard model
  • Requires design judgment or broad codebase understanding → most capable model

Handling Implementer Status

Implementer subagents report one of four statuses. Handle each appropriately:

DONE: Proceed to spec compliance review.

DONE_WITH_CONCERNS: The implementer completed the work but flagged doubts. Read the concerns before proceeding. If the concerns are about correctness or scope, address them before review. If they're observations (e.g., "this file is getting large"), note them and proceed to review.

NEEDS_CONTEXT: The implementer needs information that wasn't provided. Provide the missing context and re-dispatch.

BLOCKED: The implementer cannot complete the task. Assess the blocker:

  1. If it's a context problem, provide more context and re-dispatch with the same model
  2. If the task requires more reasoning, re-dispatch with a more capable model
  3. If the task is too large, break it into smaller pieces
  4. If the plan itself is wrong, escalate to the human

Never ignore an escalation or force the same model to retry without changes. If the implementer said it's stuck, something needs to change.

Prompt Templates

  • ./implementer-prompt.md - Dispatch implementer subagent
  • ./spec-reviewer-prompt.md - Dispatch spec compliance reviewer subagent
  • ./code-quality-reviewer-prompt.md - Dispatch code quality reviewer subagent

Example Workflow

You: I'm using Subagent-Driven Development to execute this plan.

[Read plan file once: docs/superpowers/plans/feature-plan.md]
[Extract all 5 tasks with full text and context]
[Create TodoWrite with all tasks]

Task 1: Hook installation script

[Get Task 1 text and context (already extracted)]
[Dispatch implementation subagent with full task text + context]

Implementer: "Before I begin - should the hook be installed at user or system level?"

You: "User level (~/.config/superpowers/hooks/)"

Implementer: "Got it. Implementing now..."
[Later] Implementer:
  - Implemented install-hook command
  - Added tests, 5/5 passing
  - Self-review: Found I missed --force flag, added it
  - Committed

[Dispatch spec compliance reviewer]
Spec reviewer: ✅ Spec compliant - all requirements met, nothing extra

[Get git SHAs, dispatch code quality reviewer]
Code reviewer: Strengths: Good test coverage, clean. Issues: None. Approved.

[Mark Task 1 complete]

Task 2: Recovery modes

[Get Task 2 text and context (already extracted)]
[Dispatch implementation subagent with full task text + context]

Implementer: [No questions, proceeds]
Implementer:
  - Added verify/repair modes
  - 8/8 tests passing
  - Self-review: All good
  - Committed

[Dispatch spec compliance reviewer]
Spec reviewer: ❌ Issues:
  - Missing: Progress reporting (spec says "report every 100 items")
  - Extra: Added --json flag (not requested)

[Implementer fixes issues]
Implementer: Removed --json flag, added progress reporting

[Spec reviewer reviews again]
Spec reviewer: ✅ Spec compliant now

[Dispatch code quality reviewer]
Code reviewer: Strengths: Solid. Issues (Important): Magic number (100)

[Implementer fixes]
Implementer: Extracted PROGRESS_INTERVAL constant

[Code reviewer reviews again]
Code reviewer: ✅ Approved

[Mark Task 2 complete]

...

[After all tasks]
[Dispatch final code-reviewer]
Final reviewer: All requirements met, ready to merge

Done!

Advantages

vs. Manual execution:

  • Subagents follow TDD naturally
  • Fresh context per task (no confusion)
  • Parallel-safe (subagents don't interfere)
  • Subagent can ask questions (before AND during work)

vs. Executing Plans:

  • Same session (no handoff)
  • Continuous progress (no waiting)
  • Review checkpoints automatic

Efficiency gains:

  • No file reading overhead (controller provides full text)
  • Controller curates exactly what context is needed
  • Subagent gets complete information upfront
  • Questions surfaced before work begins (not after)

Quality gates:

  • Self-review catches issues before handoff
  • Two-stage review: spec compliance, then code quality
  • Review loops ensure fixes actually work
  • Spec compliance prevents over/under-building
  • Code quality ensures implementation is well-built

Cost:

  • More subagent invocations (implementer + 2 reviewers per task)
  • Controller does more prep work (extracting all tasks upfront)
  • Review loops add iterations
  • But catches issues early (cheaper than debugging later)

Red Flags

Never:

  • Start implementation on main/master branch without explicit user consent
  • Let an implementer improvise around a missing API — if the plan references a non-existent method, escalate to blueprint revision, not creative substitution
  • Accept "I used a similar API instead" as a resolution — the spec must be updated first
  • Skip reviews (spec compliance OR code quality)
  • Proceed with unfixed issues
  • Dispatch multiple implementation subagents in parallel (conflicts)
  • Make subagent read plan file (provide full text instead)
  • Skip scene-setting context (subagent needs to understand where task fits)
  • Ignore subagent questions (answer before letting them proceed)
  • Accept "close enough" on spec compliance (spec reviewer found issues = not done)
  • Skip review loops (reviewer found issues = implementer fixes = review again)
  • Let implementer self-review replace actual review (both are needed)
  • Start code quality review before spec compliance is ✅ (wrong order)
  • Move to next task while either review has open issues

If subagent asks questions:

  • Answer clearly and completely
  • Provide additional context if needed
  • Don't rush them into implementation

If reviewer finds issues:

  • Implementer (same subagent) fixes them
  • Reviewer reviews again
  • Repeat until approved
  • Don't skip the re-review

If subagent fails task:

  • Dispatch fix subagent with specific instructions
  • Don't try to fix manually (context pollution)

Integration

Required workflow skills:

  • superpowers:vault - REQUIRED: Set up isolated workspace before starting
  • superpowers:blueprint - Creates the plan this skill executes
  • superpowers:tribunal - Code review template for reviewer subagents
  • superpowers:seal - Complete development after all tasks

Subagents should use:

  • superpowers:forge - Subagents follow TDD for each task

Alternative workflow:

  • superpowers:exodus - Use for parallel session instead of same-session execution

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Phantom AI skill do?

Use when executing implementation plans with independent tasks in the current session

Why use Phantom on TypingMind?

Because you install it once and use it with any model. Phantom is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Phantom in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/phantom. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Phantom?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Phantom?

As many as you like. As long as a model supports skills, you can use Phantom with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Phantom AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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