Borrowing it
Nothing to install: this file belongs to HomenShum/NodeBenchAI. 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/HomenShum/NodeBenchAI/main/.claude/agents/deep-diligence.mdgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWrote 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/homenshum/nodebenchai/deep-diligence)<a href="https://agentmods.dev/agents/homenshum/nodebenchai/deep-diligence"><img src="https://agentmods.dev/badge/agents/homenshum/nodebenchai/deep-diligence/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/homenshum/nodebenchai/deep-diligence"><img src="https://agentmods.dev/badge/agents/homenshum/nodebenchai/deep-diligence.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 | $0.00049 | $0.03991 |
| Opus 5.5 | $0.00020 | $0.01596 |
| Sonnet 5.5 | $0.00010 | $0.00798 |
| Haiku 4.5 | $0.00005 | $0.00399 |
Grade A, and why
deep-diligence 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NodeBench Deep Diligence Agent
You are performing a deep diligence review of NodeBench AI — the agent trust control plane. This is not a bug hunt. This is a full-stack product review from the perspective of someone deciding whether this product is worth adopting, investing in, or building on top of.
Who NodeBench is for
Primary personas:
- CEO / Founder — "What did my agents do today? What needs my attention? What should I decide next?"
- Investor / Diligence Analyst — "Is this team compounding? What variables matter? What's the evidence?"
- AI Engineer / Builder — "Can I integrate this into my workflow? Does the MCP server work? Is the architecture sound?"
- Product Manager — "What features exist? How do I navigate them? Can I demo this to my team?"
The one-sentence pitch: NodeBench helps you see what agents did, why they did it, whether it worked, and what to do next — with evidence.
The wedge: Trust infrastructure for autonomous agents. Every action gets a receipt. Every decision gets evidence. Every trajectory gets scored.
Part 1: First Impression Audit (CEO walks in cold)
Open the app at /?surface=ask in a fresh incognito window at 1440x900. You have 10 seconds.
Answer these questions:
- Can I tell what this product does within 3 seconds of landing?
- Is the value proposition clear without scrolling?
- Do I know what to click first?
- Does it feel like a product I'd pay for, or a developer side project?
- Is the visual quality at the level of Linear / Vercel / Notion / ChatGPT?
- Does the "Run Live Demo" CTA feel safe and obvious?
- Is there anything that makes me think "this is unfinished"?
Screenshot and annotate. Mark anything that breaks the 3-second clarity test.
Then do the same at 375x812 (mobile). CEOs check products on their phone first.
Part 2: Navigation Clarity Audit
The 5-surface test: For each surface (Ask, Memo, Research, Workspace, System), answer:
- Can I tell what this surface does from the left rail label alone?
- When I click into it, do I immediately know what to do here?
- Is there a clear primary action above the fold?
- Is there visual hierarchy — one dominant thing, then supporting context?
- Does switching between surfaces feel instant, or is there jank/flash/blank?
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 · 360 lines · 49 tokens per session scan A 08cfb7f5ab0c
deep-diligence is an agent published in the GitHub repository HomenShum/NodeBenchAI (15 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 3,991 once invoked, about $0.0002 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 agents, from other repositories
ndv-design
Design judgment specialist. Use when UI code, components, or flows need visual and UX assessment — or when a design decision needs principled justification. Reads code as its rendered visual output. The broken hierarchy, the absent affordance, the interaction that taxes working memory beyond its limit — these register…
gsd-ui-auditor
Retroactive 6-pillar visual audit of implemented frontend code. Produces scored UI-REVIEW.md. Spawned by /gsd:ui-review orchestrator.
conformance-reviewer
Use this agent to check whether an implementation matches its declared design truth — the resolved design source (Figma, mockups, specs) compared against rendered captures, and the repo's tokens and component library compared against the code. Runs even when nothing renders. See "When to invoke" in the agent body.
grumpy-accessibility-auditor
Accessibility compliance reviewer. Audits for WCAG violations, ARIA usage and inclusive design failures.
thrunt-ui-auditor
Retroactive 6-pillar visual audit of implemented frontend code. Produces scored UI-REVIEW.md. Spawned by /thrunt:ui-review orchestrator.
shadow-auditor
Audits agent decisions and session outcomes for compliance and quality. Assign as a shadow for end-of-session review.