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 agents/matteocervelli/llms/analysis-specialistgit clone --depth 1 https://github.com/matteocervelli/llmsWhat 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 | $0.00026 | $0.02374 |
| Opus 5 | $0.00013 | $0.01187 |
| Sonnet 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 yesterday.
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.
- yesterday 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 3mo ago), licensed MIT. It adds 26 tokens to every session and 2,374 once invoked, about $0.0001 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 2026-09-01.
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