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 skills add membranedev/application-skills --skill aleph-alphagit clone --depth 1 https://github.com/membranedev/application-skillsWrote 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/membranedev/application-skills/aleph-alpha)<a href="https://agentmods.dev/skills/membranedev/application-skills/aleph-alpha"><img src="https://agentmods.dev/badge/skills/membranedev/application-skills/aleph-alpha/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/membranedev/application-skills/aleph-alpha"><img src="https://agentmods.dev/badge/skills/membranedev/application-skills/aleph-alpha.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 | $0.00031 | $0.01636 |
| Opus 5.5 | $0.00012 | $0.00654 |
| Sonnet 5.5 | $0.00006 | $0.00327 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
aleph-alpha 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 176 lines · 31 tokens per session scan A 49835ae0b6c7
aleph-alpha is a skill published in the GitHub repository membranedev/application-skills (270 stars, last pushed 6mo ago), with no licence file. It adds 31 tokens to every session and 1,636 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 2026-09-03.
Other skills, from other repositories
ai-product-canvas
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation…
prompt-regression-suite
Design a regression test suite that catches an LLM feature getting worse when the prompt, model, or context changes. Use when asked to stop prompt changes breaking production, set up golden tests or CI gates for an LLM feature, or test a model/prompt upgrade before shipping it. Produces a golden case set, per-case…
ai-output-verifier
Check AI output before you trust or use it — where it's likely wrong, what to verify, and how to catch confident-sounding errors. Use when asked can I trust this AI answer, how do I verify what AI told me, fact-check this AI output, or is this AI response reliable. Produces a risk read on the specific output (the…
delegate-to-ai
Decide what in your workload to hand to AI and what to keep yourself — like managing a fast, capable, but unreliable new hire — so you get leverage without offloading the things that need you. Use when asked what should I delegate to AI, what can AI take off my plate, where should I use AI in my work, or what should I…
prompt-library-builder
Build a personal library of reusable prompts for the things you ask AI again and again — so you stop rewriting the same request from scratch. Use when asked help me build a prompt library, save my best prompts, I keep writing the same prompts, or organize my AI prompts. Produces a captured set of your recurring AI…
spot-ai-mistakes
Learn to recognize where and how AI tends to go wrong — the specific failure patterns — so you catch its mistakes on sight instead of getting burned by confident errors. Use when asked how do I know when AI is wrong, what are AI's common mistakes, how do I catch AI errors, or where does AI mess up. Produces the…