gh-doodledood-claude-code-plugins-claude-plugins-consultant/skills/consultant/SKILL.md

name, description

name

description

consultant

Consult with powerful AI models via Python/LiteLLM for complex analysis, architectural reviews, security audits, or comprehensive code understanding. Supports any LiteLLM-compatible model (100+ providers) with custom base URLs. Use when you need deeper insights: (1) Complex architectural decisions, (2) Security vulnerability analysis, (3) Comprehensive code reviews across large codebases, (4) Understanding intricate patterns in unfamiliar code, (5) Expert-level domain analysis. Runs asynchronously with session management.

Consultant

Overview

Consultant is a Python-based tool using LiteLLM to provide access to powerful AI models for complex analysis tasks. It accepts file globs and prompts, runs asynchronously, and returns detailed insights after extended reasoning time.

Key advantages:

• Supports 100+ LLM providers through LiteLLM (OpenAI, Anthropic, Google, Azure, local models, etc.)
• Custom base URLs for any provider or local LLM server
• Automatic model discovery and selection
• Async operation with session management
• Token counting and context overflow protection
• Cross-platform Python implementation

Requirements

The CLI uses uv for automatic dependency management. Dependencies (litellm, requests) are installed automatically on first run via PEP 723 inline script metadata.

If uv is not installed:

curl -LsSf https://astral.sh/uv/install.sh | sh

Getting Started

IMPORTANT: Always run uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py --help first to understand current capabilities.

Where {CONSULTANT_SCRIPTS_PATH} is the path to claude-plugins/consultant/skills/consultant/scripts/

Note: Always use uv run --upgrade to ensure litellm is at the latest version. This is important because LiteLLM frequently adds support for new models.

Basic Usage

Start a Consultation

The consultant script runs synchronously (blocking until completion). For long-running analyses, you should run it in the background using the Bash tool with run_in_background: true, then use BashOutput to check progress every 30 seconds until completion.

Example: Running in background via Bash tool

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Analyze this code for security vulnerabilities" \
  --file src/**/*.py \
  --slug "security-audit"

When calling via the Bash tool:

1. Use run_in_background: true parameter
2. Wait at least 30 seconds, then use BashOutput tool with the returned bash_id to check progress
3. If still running, wait another 30 seconds and check again - repeat until completion
4. The script will print output as it completes each step
5. Final results appear after "Waiting for completion..." message

What you'll see:

• Token usage summary
• Session ID
• "Waiting for completion..." status
• Streaming output from the LLM
• Final results after completion

Check Session Status

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py session security-audit

This returns JSON with:

• Current status (running/completed/error)
• Full output if completed
• Error details if failed

List All Sessions

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py list

Shows all sessions with status, timestamps, and models used.

Advanced Features

Custom Provider with Base URL

# Use custom LiteLLM endpoint
uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Review this PR" \
  --file src/**/*.ts \
  --slug "pr-review" \
  --base-url "http://localhost:8000" \
  --model "gpt-5.1"

List Available Models

From Custom Provider (with Base URL)

Query models from a custom LiteLLM endpoint:

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py models \
  --base-url "http://localhost:8000"

What happens:

• Sends HTTP GET to http://localhost:8000/v1/models
• Parses JSON response with model list
• Returns all available models from that endpoint
• Example output:

  [
    {"id": "gpt-5.1", "created": 1234567890, "owned_by": "openai"},
    {"id": "claude-sonnet-4-5", "created": 1234567890, "owned_by": "anthropic"}
  ]
  

From Known Providers (without Base URL)

Query known models from major providers:

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py models

What happens:

• Returns hardcoded list of known models (no API call)
• Includes models from OpenAI, Anthropic, Google
• Example output:

  [
    {"id": "gpt-5.1", "provider": "openai"},
    {"id": "claude-sonnet-4-5", "provider": "anthropic"},
    {"id": "gemini/gemini-2.5-flash", "provider": "google"}
  ]
  

Automatic Model Selection

Scenario 1: With Base URL (custom provider)

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Architectural review" \
  --file "**/*.py" \
  --slug "arch-review" \
  --base-url "http://localhost:8000"
  # No --model flag

Consultant will:

1. Query http://localhost:8000/v1/models to get available models
2. Select a model based on the task requirements

For model selection guidance: Check https://artificialanalysis.ai for up-to-date model benchmarks and rankings to choose the best model for your use case.

Scenario 2: Without Base URL (default providers)

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Code review" \
  --file src/*.py \
  --slug "review"
  # No --model flag, no --base-url flag

Consultant will:

1. Use known models list (OpenAI, Anthropic, Google)
2. Select a model based on task requirements

For model selection guidance: Check https://artificialanalysis.ai for up-to-date model benchmarks and rankings. Recommended defaults: gpt-5-pro, claude-opus-4-5-20251101, gemini/gemini-3-pro-preview.

Scenario 3: Explicit Model (no auto-selection)

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Bug analysis" \
  --file src/*.py \
  --slug "bug" \
  --model "gpt-5.1"

Consultant will:

1. Skip model querying and scoring
2. Use gpt-5.1 directly
3. Use default provider for GPT-5 (OpenAI)
4. No "Selected model" message

Specify API Key

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "..." \
  --file ... \
  --slug "..." \
  --api-key "your-api-key"

Or use environment variables (see below).

Environment Variables

Consultant checks these environment variables:

API Keys (checked in order):

• LITELLM_API_KEY: Generic LiteLLM API key
• OPENAI_API_KEY: For OpenAI models
• ANTHROPIC_API_KEY: For Claude models

Base URL:

• OPENAI_BASE_URL: Default base URL (used if --base-url not provided)

Example:

# Set API key
export LITELLM_API_KEY="your-key-here"

# Optional: Set default base URL
export OPENAI_BASE_URL="http://localhost:8000"

# Now consultant will use the base URL automatically
uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py --prompt "..." --file ... --slug "..."

When to Use Consultant

Perfect for:

• Complex architectural decisions requiring deep analysis
• Security vulnerability analysis across large codebases
• Comprehensive code reviews before production deployment
• Understanding intricate patterns or relationships in unfamiliar code
• Expert-level domain analysis (e.g., distributed systems, concurrency)

Don't use consultant for:

• Simple code edits or fixes you can handle directly
• Questions answerable by reading 1-2 files
• Tasks requiring immediate responses (consultant takes minutes)
• Repetitive operations better suited to scripts

Session Management

Session Storage

Sessions are stored in ~/.consultant/sessions/{session-id}/ with:

• metadata.json: Status, timestamps, token counts, model info
• prompt.txt: Original user prompt
• output.txt: Streaming response (grows during execution)
• error.txt: Error details (if failed)
• file_*: Copies of all attached files

Reattachment

Query status anytime:

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py session <slug>

The most recent session with that slug will be returned.

Cleanup

Sessions persist until manually deleted:

rm -rf ~/.consultant/sessions/{session-id}

Token Management

Consultant automatically:

1. Counts tokens for prompt and each file
2. Validates against model's context size
3. Reserves 20% of context for response
4. Fails fast with clear errors if over limit

Example output:

📊 Token Usage:
- Prompt: 1,234 tokens
- Files: 45,678 tokens (15 files)
- Total: 46,912 tokens
- Limit: 128,000 tokens
- Available: 102,400 tokens (80%)

If context exceeded:

ERROR: Input exceeds context limit!
  Input: 150,000 tokens
  Limit: 128,000 tokens
  Overage: 22,000 tokens

Suggestions:
1. Reduce number of files (currently 25)
2. Use a model with larger context
3. Shorten the prompt

Model Selection

Automatic Selection Algorithm

When no model is specified, consultant:

1. Queries available models from provider (via /v1/models or known list)
2. Scores each model based on:
   • Version number (GPT-5 > GPT-4 > GPT-3.5)
   • Capability tier (opus/pro > sonnet > haiku)
   • Context size (200k > 128k > 32k)
   • Reasoning capability (o1/o3 models higher)
3. Selects the highest-scoring model

Supported Providers

Through LiteLLM, consultant supports:

• OpenAI (GPT-4, GPT-5, o1, etc.)
• Anthropic (Claude Sonnet 4, Opus 4, etc.)
• Google (Gemini 3, 2.5, etc.)
• Azure OpenAI
• AWS Bedrock
• Cohere
• HuggingFace
• Local models (Ollama, vLLM, LM Studio, etc.)
• Any OpenAI-compatible API

Error Handling

Consultant provides clear error messages for common issues:

Missing API Key

ERROR: No API key provided.
Set LITELLM_API_KEY environment variable or use --api-key flag.

Context Limit Exceeded

ERROR: Input exceeds context limit!
[Details and suggestions]

Model Not Found

ERROR: Model 'gpt-7' not found at base URL
Available models: [list]

Network Failure

WARNING: Network error connecting to http://localhost:8000
Retrying in 5 seconds... (attempt 2/3)

Troubleshooting

Issue: uv: command not found

Solution:

curl -LsSf https://astral.sh/uv/install.sh | sh

Issue: ImportError: No module named 'litellm'

Solution: This shouldn't happen with uv run, but if it does, clear uv cache:

uv cache clean

Issue: Session stuck in "running" status

Solution:

• Check session directory: ls ~/.consultant/sessions/{session-id}/
• Look for error.txt: cat ~/.consultant/sessions/{session-id}/error.txt
• Check process is running: ps aux | grep consultant_cli.py

Issue: Context limit exceeded

Solution:

1. Reduce number of files attached
2. Use a model with larger context (e.g., claude-3-opus has 200k)
3. Shorten the prompt
4. Split into multiple consultations

Issue: Model discovery fails

Solution:

• Explicitly specify a model with --model
• Check base URL is correct: curl http://localhost:8000/v1/models
• Verify API key is set correctly

Examples

Security Audit

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Identify SQL injection vulnerabilities in the authentication module. For each finding, provide: vulnerable code location, attack vector, and recommended fix." \
  --file "apps/*/src/**/*.{service,controller}.ts" \
  --slug "security-audit" \
  --model "claude-sonnet-4-5"

Architectural Review

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Identify the top 5 highest-impact architectural issues causing tight coupling. For each: explain the problem, show affected components, and recommend a solution." \
  --file "apps/*/src/**/*.ts" \
  --slug "arch-review"

PR Review

# Generate diff first
git diff origin/main...HEAD > /tmp/pr-diff.txt

uv run --upgrade {CONSULTANT_SCRIPTS_PATH}/consultant_cli.py \
  --prompt "Review this PR for production deployment. Flag blockers, high-risk changes, and suggest regression tests." \
  --file /tmp/pr-diff.txt \
  --slug "pr-review"

Integration with Consultant Agent

The consultant agent uses this Python CLI automatically. When you invoke:

• /consultant-review
• /consultant-investigate-bug
• /consultant-execplan

The agent constructs the appropriate consultant_cli.py command with all necessary files and prompt.

Resources

• LiteLLM Documentation
• Supported Models
• Consultant Plugin README
• Glob Patterns Guide