deep-analysis

A deep codebase exploration and synthesis workflow using several cooperating AI agents. It divides investigation into focused areas and combines the findings into one result.

In plain words
What is it for?
Reconnaissance, focused code exploration, team planning, and producing a combined analysis of how a codebase works.
Why use it?
It provides a structured way to understand large or unfamiliar codebases without relying on one narrow investigation.

Skill for Claude CodeCodex

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 skills/sequenzia/agent-alchemy/deep-analysis
Any agent
npx skills add sequenzia/agent-alchemy --skill deep-analysis
Clone the repo
git clone --depth 1 https://github.com/sequenzia/agent-alchemy

Made for: Claude Code, Codex.

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,158 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 $0.00052 $0.06158
Opus 5 $0.00026 $0.03079
Sonnet 5 $0.00010 $0.01232
Haiku 4.5 $0.00005 $0.00616

Measured 2d ago against content hash 8bbe3313e76c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-analysis 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 2d 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.

claude/core-tools/skills/deep-analysis/SKILL.md · 522 lines

How it starts

The opening of the file, as written. The whole thing — 522 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Analysis Workflow

Execute a structured exploration + synthesis workflow using Agent Teams with hub-and-spoke coordination. The lead performs rapid reconnaissance to generate dynamic focus areas, composes a team plan for review, workers explore independently, and a synthesizer merges findings with Bash-powered investigation.

This skill can be invoked standalone or loaded by other skills as a reusable building block. Approval behavior is configurable via .claude/agent-alchemy.local.md.

Settings Check

Goal: Determine whether the team plan requires user approval before execution.

  1. Read settings file:

    • Check if .claude/agent-alchemy.local.md exists
    • If it exists, read it and look for a deep-analysis section with nested settings:
      - **deep-analysis**:
        - **direct-invocation-approval**: true
        - **invocation-by-skill-approval**: false
      
    • If the file does not exist or is malformed, use defaults (see step 4)
  2. Determine invocation mode:

    • Direct invocation: The user invoked /deep-analysis directly, or you are running this skill standalone
    • Skill-invoked: Another skill (e.g., codebase-analysis, feature-dev, docs-manager) loaded and is executing this workflow
  3. Resolve settings:

    • If settings were found, use them as-is
    • If the file is missing or the deep-analysis section is absent, use defaults:
      • direct-invocation-approval: true
      • invocation-by-skill-approval: false
    • If the file exists but is malformed (unparseable), warn the user and use defaults
  4. Set REQUIRE_APPROVAL:

    • If direct invocation → use direct-invocation-approval value (default: true)
    • If skill-invoked → use invocation-by-skill-approval value (default: false)
  5. Parse session settings (also under the deep-analysis section):

    - **deep-analysis**:
      - **cache-ttl-hours**: 24
      - **enable-checkpointing**: true
      - **enable-progress-indicators**: true
    
    • cache-ttl-hours: Number of hours before exploration cache expires. Default: 24. Set to 0 to disable caching entirely.
    • enable-checkpointing: Whether to write session checkpoints at phase boundaries. Default: true.
    • enable-progress-indicators: Whether to display [Phase N/6] progress messages. Default: true.

Read the full file on GitHub · 522 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. 2d ago First seen · 522 lines · 52 tokens per session scan A 8bbe3313e76c

Subscribe to this mod's changes

deep-analysis is a skill published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 6,158 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-30.

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