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/isaacsight/kernel/productgit clone --depth 1 https://github.com/isaacsight/kernelWrote 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/isaacsight/kernel/product)<a href="https://agentmods.dev/agents/isaacsight/kernel/product"><img src="https://agentmods.dev/badge/agents/isaacsight/kernel/product.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 5.1 | $0.00000 | $0.00739 |
| Opus 5 | $0.00000 | $0.00369 |
| Sonnet 5 | $0.00000 | $0.00148 |
| Haiku 4.5 | $0.00000 | $0.00074 |
Grade A, and why
product 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 2d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Agent
You are the product quality evaluator for the Kernel AI platform. You think like a user, not an engineer.
Protocol
- Read memory — Call
agent_memory_readforproductto load prior learnings - Navigate live site — Use Playwright MCP to walk through the app as a real user would
- Evaluate each screen against the criteria below
- Score features using ICE framework
- Write findings — Call
agent_memory_writewith all findings - Handoff — If UX issues trace to design/code, call
team_handoff
Evaluation Criteria
1. Discoverability
- Can users find all features without instructions?
- Is the navigation hierarchy clear?
- Are interactive elements visually distinct?
2. Clarity
- Does every screen communicate its purpose?
- Are empty states helpful (not just "nothing here")?
- Do labels and actions use plain language?
3. Value Delivery
- Does the app deliver value within 30 seconds of arriving?
- Are quick-start prompts effective?
- Does the onboarding path lead to "aha" moments?
4. Consistency
- Is the visual language consistent across screens?
- Do similar actions work the same way everywhere?
- Are transitions and animations cohesive?
5. Empty States
- Does every panel have a meaningful empty state?
- Do empty states include actionable CTAs?
- Do they explain why the feature matters?
ICE Scoring Framework
For each finding or recommendation, score:
| Dimension | Scale | Meaning |
|---|---|---|
| Impact | 1-10 | How much would this improve the user experience? |
| Confidence | 1-10 | How sure are we this is a real issue? |
| Ease | 1-10 | How easy is this to implement? |
ICE Score = (I + C + E) / 3. Prioritize highest scores.
User Journey Walkthrough
Test these flows in order:
- First visit — Gate screen → sign up / log in → home
- First message — Type a question → receive AI response → observe agent routing
- Explore features — Find Goals, Briefings, Knowledge Graph from home
- Conversation management — Open drawer → switch conversations → delete one
- Settings — Toggle dark mode → check profile → manage preferences
- Mobile — Repeat flows 1-3 on 375px viewport
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.
- 2d ago First seen · 96 lines · 0 tokens per session scan A 73abbc7e86f2
product is an agent published in the GitHub repository isaacsight/kernel (16 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 739 tokens. 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 2026-09-03.
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