analyze

analyze is a command for Claude Code from terrene-foundation/metis. It costs 15 tokens per session (1,403 once invoked), scanned A, a copy of analyze, Apache-2.0.

A command that starts the analysis phase of a project workflow. It finds the relevant workspace, reads the project's briefs, checks the phase, and prepares locations for research, plans, and user flows.

In plain words
What is it for?
Use it to begin product analysis, organize research outputs, and prepare planning and user-flow documents for a workspace.
Why use it?
It gives an agent a defined starting point and ensures the user's project brief is considered before recommendations are made.

Command for Claude Code

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/terrene-foundation/metis/analyze
Clone the repo
git clone --depth 1 https://github.com/terrene-foundation/metis

Made for: Claude Code.

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 analyze

README.md
[![agentmods](https://agentmods.dev/badge/commands/terrene-foundation/metis/analyze.svg)](https://agentmods.dev/commands/terrene-foundation/metis/analyze)
Your own site
<a href="https://agentmods.dev/commands/terrene-foundation/metis/analyze"><img src="https://agentmods.dev/badge/commands/terrene-foundation/metis/analyze.svg" alt="Measured on agentmods" height="20"></a>
Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,403 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 88% copy Near-identical to another mod 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 $0.00015 $0.01403
Opus 5 $0.00008 $0.00701
Sonnet 5 $0.00003 $0.00281
Haiku 4.5 $0.00002 $0.00140

Measured 5d ago against content hash 961551a15d57, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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.

Origin

This is a copy

88% identical to analyze — 27 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/commands/analyze.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workspace Resolution

  1. If $ARGUMENTS specifies a project name, use workspaces/$ARGUMENTS/
  2. Otherwise, use the most recently modified directory under workspaces/ (excluding instructions/)
  3. If no workspace exists, ask the user to create one first
  4. Read all files in workspaces/<project>/briefs/ for user context (this is the user's input surface)

Phase Check

  • Output goes into workspaces/<project>/01-analysis/, workspaces/<project>/02-plans/, and workspaces/<project>/03-user-flows/

Execution Model

This phase executes under the autonomous execution model (see rules/autonomous-execution.md). All analysis, deliberation, and recommendations MUST assume autonomous AI agent execution — not human team constraints. Do not estimate effort in human-days. Do not constrain recommendations by team size or hiring. Recommend the technically optimal approach; agents scale horizontally.

Workflow

1. Be explicit about objectives and expectations

Understand the product idea before diving into research.

2. Perform Deep Research

Document in detail in workspaces/<project>/01-analysis/01-research.

  • Use as many subdirectories and files as required
  • Name them sequentially as 01-, 02-, etc, for easy referencing

3. Ensure strong product focus

Keep this soft rule in mind for everything:

  • 80% of the codebase/features/efforts can be reused (agnostic)
  • 15% of client specific requirements goes into consideration for self-service functionalities that can be reused (agnostic)
  • 5% customization

Steps:

  1. Research thoroughly and distill value propositions and UNIQUE SELLING POINTS
    • Scrutinize and critique the intent and vision, focusing on perfect product-market fit
    • Research competing products, gaps, painpoints, and any other information that helps build solid value propositions
    • Define unique selling points (not the same as value propositions) — be extremely critical and scrutinize them
  2. Evaluate using platform model thinking
    • Seamless direct transactions between users (producers, consumers, partners)
      • Producers: Users who offer/deliver a product or service
      • Consumers: Users who consume a product or service
      • Partners: To facilitate the transaction between producers and consumers
  3. Evaluate using the AAA framework
    • Automate: Reduce operational costs
    • Augment: Reduce decision-making costs
    • Amplify: Reduce expertise costs (for scaling)
  4. Features must cover network behaviors for strong network effects
    • Accessibility: Easy for users to complete a transaction (activity between producer and consumer, not necessarily monetary)
    • Engagement: Information useful to users for completing a transaction
    • Personalization: Information curated for an intended use
    • Connection: Information sources connected to the platform (one or two-way)
    • Collaboration: Producers and consumers can jointly work seamlessly

Read the full file on GitHub · 124 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 · 124 lines · 15 tokens per session scan A 961551a15d57

Subscribe to this mod's changes

analyze is a command published in the GitHub repository terrene-foundation/metis (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,403 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to analyze, differing in 27 lines, and is treated as a copy.