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/cheatsheet/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/cheatsheet)<a href="https://agentmods.dev/skills/vgrss/acumen/cheatsheet"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/cheatsheet/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/cheatsheet"><img src="https://agentmods.dev/badge/skills/vgrss/acumen/cheatsheet.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 | $0.00020 | $0.00772 |
| Opus 5.5 | $0.00008 | $0.00309 |
| Sonnet 5.5 | $0.00004 | $0.00154 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
cheatsheet 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Print a compact cheatsheet of every Acumen skill so the user can see what's available at a glance.
Mindset
You are a helpful reference card. No analysis, no opinions — just a clean, scannable list of every command the user can run, what it does, and when to reach for it. The list must reflect the skills that actually exist right now, not a memorized set.
Build the list (do this every time — never print from memory)
- Enumerate skills. List the skill directories:
ls -1 source/skills/. Each directory is one skill with asource/skills/{name}/SKILL.md. - Read each skill's frontmatter. For every skill, read its
SKILL.mdYAML frontmatter and capturename,description, and whetheruser-invocable: trueis present.- Skills without
user-invocable: trueare core skills invoked by other skills (e.g.product-thinking). Omit them from the cheatsheet — they are not commands the user runs directly.
- Skills without
- Group by layer. Read the "Skill layers" section of
CLAUDE.mdand use it as the grouping and ordering source. Current layers: Context, Audit, Ideate, Craft, Communicate, Meta.- Assign each user-invocable skill to its layer per
CLAUDE.md. - If a user-invocable skill is not listed in any layer in
CLAUDE.md, place it under an OTHER group at the end rather than dropping it — a skill must never be silently omitted. (If OTHER is non-empty, it's a signal thatCLAUDE.md's Skill layers section needs updating.)
- Assign each user-invocable skill to its layer per
Output
Render the cheatsheet as a boxed ASCII card using the data you just gathered. Use this structure — one section per layer (in CLAUDE.md order), each skill as /{name} padded with dots to a short one-line summary derived from its description. Do not add commentary before or after the box.
┌─────────────────────────────────────────────────────────────┐
│ ACUMEN CHEATSHEET — Product fluency for AI │
├─────────────────────────────────────────────────────────────┤
│ │
│ CONTEXT (build product knowledge) │
│ /{name} ...... <one-line summary> │
│ ... │
│ │
│ AUDIT (assess product health) │
│ ... │
│ │
│ IDEATE (diverge on solutions) │
│ ... │
│ │
│ CRAFT (build product artifacts) │
│ ... │
│ │
│ COMMUNICATE (share with the world) │
│ ... │
│ │
│ META (manage Acumen itself) │
│ ... │
│ │
├─────────────────────────────────────────────────────────────┤
│ Tip: Run /teach-acumen first to set up product context. │
│ Then audit with /diagnose, then diverge with /workshop. │
└─────────────────────────────────────────────────────────────┘
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 · 57 lines · 20 tokens per session scan A 3491d60d6a04
cheatsheet is a skill published in the GitHub repository VGrss/Acumen (12 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 20 tokens to every session and 772 once invoked, about $0.0001 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.
cheatsheet compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cheatsheetVGrss/Acumen | Skill | 12 | 3mo ago | Apache-2.0 | A | 20 | 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 |