Borrowing it
Nothing to install: this file belongs to VGrss/Acumen. 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/VGrss/Acumen/main/.agents/skills/diagnose/SKILL.mdgit clone --depth 1 https://github.com/VGrss/AcumenWrote 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/skills/vgrss/acumen/diagnose)<a href="https://agentmods.dev/skills/vgrss/acumen/diagnose"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/diagnose/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/skills/vgrss/acumen/diagnose"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/diagnose.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 | $0.00047 | $0.02112 |
| Opus 5.5 | $0.00019 | $0.00845 |
| Sonnet 5.5 | $0.00009 | $0.00422 |
| Haiku 4.5 | $0.00005 | $0.00211 |
Grade A, and why
diagnose 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 2mo 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /product-thinking — it contains the Context Gathering Protocol and the AI Slop Test. Follow the protocol before proceeding.
Mindset
Symptoms are not problems. Requests are not needs. Bright spots are not luck. Your job is to find what is actually broken and what is quietly working and underexploited — both grounded in data and measured against the value you're supposed to deliver to each persona.
Most product problems are misdiagnosed at the framing stage. The team sees a symptom ("users don't use feature X"), jumps to a cause ("the UI is bad"), and builds a solution ("redesign the UI"). Meanwhile the real problem was that users never discovered feature X, or that it solves a problem they don't have.
And most opportunities are missed the same way — a metric is climbing, nobody asks why, and the chance to pour fuel on what's already working slips by. Teams fixate on what's broken and ignore what's working. Diagnose does both.
Your job is to slow down the jump from symptom to solution — and to surface where the product could win, not just where it's losing.
Context Pull
Before diagnosing anything, load the full picture:
- Product context. Read
.acumen.md— strategy, positioning, north star, what success looks like. - Feature inventory. Read
.acumen/features.md— what we've built, feature health, known gaps and decay. - Personas. Read
.acumen/personas.md— who we serve, their jobs, their pain points, their workarounds. - Value chain. Read
.acumen/value-chain.md— the end-to-end workflow per persona. Use it to locate where a symptom occurs or where an opportunity sits, and whether it's a step we own, assist, or don't touch. If the value chain already lists opportunities against extension points, start there. - Competitors. Read
.acumen/competitors.md— competitive context, and where the white space is.
Data Pull
Data is the ground truth for both problems and opportunities. Check .acumen/sources.md and pull real numbers:
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.
- 2mo ago First seen · 167 lines · 47 tokens per session scan A edbfba8cfb2e
diagnose is a skill published in the GitHub repository VGrss/Acumen (12 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,112 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.
diagnose compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| diagnoseVGrss/Acumen | Skill | 12 | 3mo ago | Apache-2.0 | A | 47 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |