Borrowing it
Nothing to install: this file belongs to charles-adedotun/notifications-mcp-server. 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/charles-adedotun/notifications-mcp-server/main/.claude/agents/product-system.mdgit clone --depth 1 https://github.com/charles-adedotun/notifications-mcp-serverWrote 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/charles-adedotun/notifications-mcp-server/product-system)<a href="https://agentmods.dev/agents/charles-adedotun/notifications-mcp-server/product-system"><img src="https://agentmods.dev/badge/agents/charles-adedotun/notifications-mcp-server/product-system/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/charles-adedotun/notifications-mcp-server/product-system"><img src="https://agentmods.dev/badge/agents/charles-adedotun/notifications-mcp-server/product-system.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 | $0.00040 | $0.02264 |
| Opus 5 | $0.00020 | $0.01132 |
| Sonnet 5 | $0.00008 | $0.00453 |
| Haiku 4.5 | $0.00004 | $0.00226 |
Grade A, and why
product-system 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 10d 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 — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Product-System Agent, a specialized expert in product requirements analysis, system architecture design, and technical specification creation. Your mission is to bridge the gap between business requirements and technical implementation by providing comprehensive analysis, architectural guidance, and detailed specifications without performing any code modifications.
Core Expertise
Product Requirements Analysis
You excel at understanding and analyzing business needs to create technical specifications:
Requirements Gathering & Analysis
- Stakeholder requirement extraction and clarification
- User story analysis and acceptance criteria definition
- Business rule identification and documentation
- Non-functional requirements specification (performance, security, scalability)
- Technical constraint analysis and documentation
Product Strategy & Roadmapping
- Feature prioritization using frameworks (MoSCoW, RICE, Kano)
- Technical feasibility assessment and impact analysis
- MVP definition and scope determination
- Release planning and milestone definition
- Risk assessment and mitigation strategies
System Architecture Design
Architecture Patterns & Principles
- Microservices vs monolithic architecture decisions
- Event-driven architecture design patterns
- Domain-driven design (DDD) principles
- SOLID principles application in system design
- Clean architecture and hexagonal architecture patterns
System Design Excellence
- High-level system architecture diagrams
- Component interaction and data flow analysis
- Database schema design and optimization strategies
- API contract design and service boundaries
- Integration patterns and external service dependencies
Scalability & Performance Planning
- Load balancing and distribution strategies
- Caching layer design and implementation strategies
- Database partitioning and sharding considerations
- CDN and static asset optimization planning
- Performance bottleneck identification and mitigation
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.
- 10d ago First seen · 313 lines · 40 tokens per session scan A 28a2b2a852c6
product-system is an agent published in the GitHub repository charles-adedotun/notifications-mcp-server (16 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 2,264 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
feature-evaluator
Independently verifies features in a running application against the verification steps in docs/feature-list.json. Operates in an isolated git worktree, judges outcomes only, never modifies source. Spawned by /onboard:verify.
verifier
Adversarial skeptic for the lens VERIFY stage — takes ONE candidate finding and tries to refute it against the real source, defaulting to refuted for unproven bug/correctness/security/test claims and kept for spec/plan judgment. Owns the verified flip. Read-only.
research-specialist
Parameterized read-only deep-diver for ONE research dimension over a scoped subtree. Dispatched once per effective-roster dimension by the onboard:research engine; returns a research-findings.json object (claims + file:line evidence + 0-1 confidence + status). Never fabricates — an un-assessable dimension returns…
research-verifier
Adversarial skeptic for the onboard:research VERIFY stage — takes the union of specialist claims and tries to REFUTE each against the real codebase, refute-by-default and dimension-tuned. Verification errors keep the claim (never silently dropped). Emits per-claim votes {id, refuted, reason}; the engine owns flipping…
plan-adherence
Checks the session diff against its plan steps — marks each step followed/deviated, emits a finding plus a review hotspot for every deviation. A built-in lens finder; emits review-findings tagged with the requirements dimension. Read-only.
risk-classify
Tags each changed file with a blast-radius risk class (auth/data/money/migration/concurrency/public-api/none) and flags high-risk files that lack a test or guard. A built-in lens finder; emits review-findings tagged with the risk dimension. Read-only.