deep-analyze

deep-analyze is a command for coding agents from dansasser/claude-code-marketplace. It costs 0 tokens per session (1,434 once invoked), scanned A, original, MIT.

A command for examining a file or folder from several viewpoints, such as security, architecture, implementation, testing, or performance. It uses multiple analysis workers in parallel.

In plain words
What is it for?
It is used to review source files, directories, or plans and produce focused findings across selected technical areas.
Why use it?
It brings related review concerns together so problems are less likely to be missed during a code or design investigation.

Command

Part of the ollama-agents plugin — 5 commands, 3 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/dansasser/claude-code-marketplace/deep-analyze
Clone the repo
git clone --depth 1 https://github.com/dansasser/claude-code-marketplace

Or install ollama-agents, the plugin that ships this one along with the rest of its 5 commands, 3 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-analyze

README.md
[![agentmods](https://agentmods.dev/badge/commands/dansasser/claude-code-marketplace/deep-analyze.svg)](https://agentmods.dev/commands/dansasser/claude-code-marketplace/deep-analyze)
Your own site
<a href="https://agentmods.dev/commands/dansasser/claude-code-marketplace/deep-analyze"><img src="https://agentmods.dev/badge/commands/dansasser/claude-code-marketplace/deep-analyze.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,434 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01434
Opus 5 $0.00000 $0.00717
Sonnet 5 $0.00000 $0.00287
Haiku 4.5 $0.00000 $0.00143

Measured 5d ago against content hash f477079a1334, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/claude-ollama-agents/commands/deep-analyze.md · 189 lines

How it starts

The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Multi-Perspective Analysis

Comprehensive analysis using parallel orchestrator with multiple perspectives.

Usage: /deep-analyze <file_or_directory> [perspectives]

Perspectives: (comma-separated, max 4)

  • security: Security vulnerabilities and threat modeling
  • architecture: Design patterns and structural analysis
  • implementation: Code quality and best practices
  • testing: Test coverage and validation strategies
  • performance: Bottlenecks and optimization opportunities

Examples:

  • /deep-analyze implementation-plan.md - Auto-select perspectives
  • /deep-analyze src/auth.py security,testing - Focus on security and testing
  • /deep-analyze src/ architecture,security - Directory analysis with multiple perspectives
  • /deep-analyze src/api/ security,performance - API directory deep analysis

You are performing deep multi-perspective analysis using the parallel orchestrator agent.

Target: $1 Perspectives: ${2:-auto}

Your Process:

  1. Validate Target:

    • Verify file/directory exists (use Read/Glob tools)
    • Check size and estimate tokens
    • Ensure suitable for deep analysis (not trivial files)
  2. Determine Perspectives:

    Auto-Select (if perspectives=$ARGUMENTS or empty): Based on file type:

    • Code files (.py, .js, .ts, etc.): security, quality, testing
    • Architecture docs: architecture, scalability, security
    • Implementation plans: security, architecture, implementation
    • API specs: security, architecture, performance

    User-Specified: Parse comma-separated list from $2 Validate 2-4 perspectives

  3. Invoke Parallel Orchestrator Agent:

    Use Task tool to invoke ollama-parallel-orchestrator:

    • Target file: $1
    • Perspectives: Parsed list (2-4 perspectives)
    • Agent will:
      • Decompose into parallel analyses
      • Execute concurrently
      • Track sessions
      • Synthesize results
  4. Directory Operations by Perspective:

    Each perspective uses optimal directory operations:

    Perspective Primary Operation Example
    security @./dir/:search:PATTERN @./src/:search:eval, @./src/:search:password
    architecture @./dir/:tree @./src/:tree
    implementation @./dir/:search:TODO @./src/:search:TODO, @./src/:search:FIXME
    testing @./tests/:tree @./tests/:tree, @./src/:search:test
    performance @./dir/:search:PATTERN @./src/:search:for.*in, @./src/:search:query
  5. Perspectives Explained:

    Security:

    • Vulnerabilities and attack vectors
    • Threat modeling
    • Authentication/authorization
    • Input validation
    • Secrets management
    • Directory ops: @./src/:search:eval, @./src/:search:password, @./src/:search:secret

    Architecture:

    • Design patterns
    • Structural organization
    • Separation of concerns
    • Modularity and coupling
    • Scalability considerations
    • Directory ops: @./src/:tree, @./src/:search:import

    Implementation:

    • Code quality and readability
    • Best practices adherence
    • Error handling
    • Edge case coverage
    • Refactoring opportunities
    • Directory ops: @./src/:search:TODO, @./src/:search:FIXME, @./src/:tree

    Testing:

    • Test coverage assessment
    • Testing strategy
    • Edge cases and corner cases
    • Integration points
    • Test quality
    • Directory ops: @./tests/:tree, @./src/:search:@pytest

    Performance:

    • Bottleneck identification
    • Algorithm efficiency
    • Resource utilization
    • Caching opportunities
    • Optimization recommendations
    • Directory ops: @./src/:search:for.*range, @./src/:search:while, @./src/:search:query
  6. Your Role:

    • Invoke ollama-parallel-orchestrator agent via Task tool
    • Receive comprehensive synthesized analysis
    • Format report for user
    • Highlight critical findings
    • Present prioritized recommendations

Read the full file on GitHub · 189 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 189 lines · 0 tokens per session scan A f477079a1334

Subscribe to this mod's changes

deep-analyze is a command published in the GitHub repository dansasser/claude-code-marketplace (9 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,434 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.