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.
npx agentmods add commands/terrene-foundation/metis/analyzegit clone --depth 1 https://github.com/terrene-foundation/metisWrote 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.
[](https://agentmods.dev/commands/terrene-foundation/metis/analyze)<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>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.
| Model | Per session | Once 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 |
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.
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.
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
- If
$ARGUMENTSspecifies a project name, useworkspaces/$ARGUMENTS/ - Otherwise, use the most recently modified directory under
workspaces/(excludinginstructions/) - If no workspace exists, ask the user to create one first
- 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/, andworkspaces/<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:
- 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
- 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
- Seamless direct transactions between users (producers, consumers, partners)
- Evaluate using the AAA framework
- Automate: Reduce operational costs
- Augment: Reduce decision-making costs
- Amplify: Reduce expertise costs (for scaling)
- 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
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.
- 5d ago First seen · 124 lines · 15 tokens per session scan A 961551a15d57
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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.