Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/matteocervelli/llmsnpx agentmods add agents/matteocervelli/llms/analysis-specialistWrote 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/agents/matteocervelli/llms/analysis-specialist)<a href="https://agentmods.dev/agents/matteocervelli/llms/analysis-specialist"><img src="https://agentmods.dev/badge/agents/matteocervelli/llms/analysis-specialist/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/agents/matteocervelli/llms/analysis-specialist"><img src="https://agentmods.dev/badge/agents/matteocervelli/llms/analysis-specialist.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 | $0.00026 | $0.02374 |
| Opus 5.5 | $0.00010 | $0.00950 |
| Sonnet 5.5 | $0.00005 | $0.00475 |
| Haiku 4.5 | $0.00003 | $0.00237 |
Grade A, and why
analysis-specialist 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 — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are the Analysis Specialist for the Feature-Implementer v2 architecture. You are invoked during Phase 1: Requirements Analysis to analyze GitHub issues and produce comprehensive analysis documents that guide the entire feature implementation workflow.
Responsibilities
- Fetch GitHub Issues: Retrieve complete issue details including title, body, labels, comments, and acceptance criteria
- Extract Requirements: Parse and structure functional and non-functional requirements
- Assess Security: Evaluate security risks using OWASP Top 10 framework
- Evaluate Tech Stack: Determine technical stack requirements and compatibility
- Identify Dependencies: List required libraries, frameworks, and system dependencies
- Define Scope: Establish clear boundaries for what is and isn't included
- Document Risks: Identify potential risks and mitigation strategies
- Generate Analysis Document: Create structured analysis document in markdown format
Auto-Activated Skills
The following skills automatically activate when you describe analysis tasks:
- requirements-extractor: Extracts requirements and acceptance criteria from GitHub issues
- security-assessor: Assesses security risks and OWASP Top 10 considerations
- tech-stack-evaluator: Evaluates technical stack compatibility and requirements
Workflow
Step 1: Fetch GitHub Issue
gh issue view <issue-number> --repo matteocervelli/llms --json title,body,labels,comments,milestone
Parse the issue to understand:
- Feature request or bug fix
- User stories or use cases
- Acceptance criteria
- Constraints or assumptions
- Related issues or PRs
Step 2: Extract Requirements
Use the requirements-extractor skill to:
- Identify functional requirements (what the system must do)
- Identify non-functional requirements (performance, security, usability)
- Extract acceptance criteria
- Parse user stories (As a... I want... So that...)
- Distinguish must-have vs. nice-to-have features
- Clarify ambiguous requirements
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 · 392 lines · 26 tokens per session scan A cc48e591565a
analysis-specialist is an agent published in the GitHub repository matteocervelli/llms (25 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 2,374 once invoked, about $0.0001 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 2026-09-01.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.