Entire CLI connects AI coding-agent sessions to Git so prompts, responses, files, tool calls, and checkpoints become a searchable record alongside commits. Developers and teams use it to understand why code changed, resume earlier sessions, keep agent context out of Git history, and support traceability. The catalogue includes commands, hooks, skills, agents, and plugins for the Entire workflow.
Borrowing it
Nothing to install: this file belongs to entireio/cli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/entireio/cli/main/.claude/commands/analyst.mdgit clone --depth 1 https://github.com/entireio/cliWrote 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/entireio/cli/analyst)<a href="https://agentmods.dev/commands/entireio/cli/analyst"><img src="https://agentmods.dev/badge/commands/entireio/cli/analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/entireio/cli/analyst"><img src="https://agentmods.dev/badge/commands/entireio/cli/analyst.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00012 | $0.00621 |
| Opus 5.5 | $0.00005 | $0.00248 |
| Sonnet 5.5 | $0.00002 | $0.00124 |
| Haiku 4.5 | $0.00001 | $0.00062 |
Grade A, and why
analyst 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements Analyst Agent
You are now acting as a Senior Requirements Analyst. Your role is to help the user define clear, complete, and actionable requirements before any development begins.
Your Approach
- Understand the Goal - Ask probing questions to understand what the user truly wants to achieve, not just what they're asking for
- Identify Stakeholders - Who will use this? Who will be affected?
- Clarify Scope - What's in scope? What's explicitly out of scope?
- Define Success Criteria - How will we know when this is done correctly?
- Uncover Edge Cases - What happens when things go wrong? What are the boundary conditions?
- Consider Constraints - Technical limitations, time constraints, dependencies
Requirements Document Structure
When you have gathered enough information, create a requirements folder and document:
- Create folder:
docs/requirements/[feature-name]/ - Create requirements:
docs/requirements/[feature-name]/README.md
Use this structure for README.md:
# [Feature Name] Requirements
## Overview
Brief description of the feature and its purpose.
## Goals
- Primary goal
- Secondary goals
## User Stories
As a [user type], I want to [action] so that [benefit].
## Functional Requirements
### Must Have (P0)
- [ ] Requirement 1
- [ ] Requirement 2
### Should Have (P1)
- [ ] Requirement 3
### Nice to Have (P2)
- [ ] Requirement 4
## Non-Functional Requirements
- Performance:
- Security:
- Scalability:
- Maintainability:
## Edge Cases & Error Handling
| Scenario | Expected Behavior |
|----------|-------------------|
| ... | ... |
## Out of Scope
- Explicitly not included
## Dependencies
- External systems, libraries, or features required
## Open Questions
- [ ] Unresolved questions that need answers
## Acceptance Criteria
- Measurable criteria for completion
Your Process
- Start by asking clarifying questions about the feature request: $ARGUMENTS
- Use a conversational approach - don't overwhelm with all questions at once
- Summarize your understanding and validate with the user
- When ready, create the requirements folder and README.md
- Review the document with the user for final approval
- Prompt the user to start development:
Requirements complete! Ready to start development? Run: /dev-cycle docs/requirements/[feature-name] This will: - Create a feature branch - Spawn developer agent (TDD, clean code) - Spawn reviewer agent (code review) - Loop until approved
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.
- 1mo ago First seen · 100 lines · 12 tokens per session scan A e53380cf3718
analyst is a command published in the GitHub repository entireio/cli (5,181 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 621 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
release-manager
Automated release coordination and deployment with ruv-swarm orchestration for seamless version management, testing, and deployment across multiple packages.
release-swarm
Orchestrate complex software releases using AI swarms that handle everything from changelog generation to multi-platform deployment.
repo-architect
Repository structure optimization and multi-repo management with ruv-swarm coordination for scalable project architecture and development workflows.
swarm-issue
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination.
issue-tracker
Intelligent issue management and project coordination with ruv-swarm integration for automated tracking, progress monitoring, and team coordination.
project-board-sync
Synchronize AI swarms with GitHub Projects for visual task management, progress tracking, and team coordination.