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 skills/simota/agent-skills/mintnpx skills add simota/agent-skills --skill mintgit clone --depth 1 https://github.com/simota/agent-skillsWhat 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 | $0.00030 | $0.05489 |
| Opus 5 | $0.00015 | $0.02745 |
| Sonnet 5 | $0.00006 | $0.01098 |
| Haiku 4.5 | $0.00003 | $0.00549 |
Grade A, and why
mint 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 3d 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 — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mint
"Every great test begins with great data. Mint stamps it fresh."
You are a test data architect. You design factories, generate fixtures, and produce realistic synthetic data so every test starts from a known, representative state. You believe good test data is not random — it is intentionally crafted to reveal the bugs hiding at the edges.
Principles: Type safety first · FK integrity always · Deterministic reproducibility · Boundary-driven edge coverage · PII-free by default
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- _common 10 B
- ARCHIVED_2026-08-20.md 1.1 KB
- reference/_common 13 B
- reference/anonymization.md 6.8 KB
- reference/autorun-schema.md 891 B
- reference/boundary-values.md 5.1 KB
- reference/factory-patterns.md 6.3 KB
- reference/handoffs.md 6.2 KB
- reference/llm-generated-fixtures.md 7.7 KB
- reference/multi-language.md 4.7 KB
- reference/pii-masking-deidentification.md 7.2 KB
- reference/property-based-generators.md 5.5 KB
- reference/radar 11 B
- reference/replay-production-scrub.md 7.7 KB
- reference/seed-management.md 4.7 KB
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.
- 3d ago First seen · 406 lines · 30 tokens per session scan A fed122e03450
mint is a skill published in the GitHub repository simota/agent-skills (75 stars, last pushed 9d ago), licensed MIT. It adds 30 tokens to every session and 5,489 once invoked, about $0.0002 per session on Opus 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 skills, from other repositories
plan
Use when a request needs shaping before any code is written — a rough or vague prompt to sharpen, an ambiguous idea to design, or a clear-enough task to decompose. One chain-starter that amplifies the prompt, designs the approach, and decomposes it into a batched task file, skipping whichever phases the request…
ai-instruction-detox
AI 指令排毒與規則治理：把散落在 CLAUDE.md、AGENTS.md、skills、context、memory 的規則 原子化、查衝突、去重複、找過時、揪 Prompt Injection，產出可套用的精簡架構與回復方案。 觸發時機：用戶說「指令排毒」「規則太亂」「CLAUDE.md 太長」「規則互相矛盾」「上下文減肥」 「AI 設定治理」「多個 Agent 規則分裂」「context 膨脹」，或要求審查／清理 AI 指令檔。 不要觸發：一般程式重構、產品程式碼審查、單純想縮短一份文件（那是編輯不是治理）。.
fellowship
Multi-task orchestrator. Coordinates agent teammates (led by Gandalf) running /quest (code) or /scout (research) workflows. Use when you have multiple independent tasks to run in parallel.
quest
Use for any non-trivial task. Orchestrates the Research-Plan-Implement cycle with compaction between phases, integrating council, lembas, gather-lore, and warden. Enforces discipline and phase gates.
retro
Post-fellowship retrospective analysis. Collects gate history, palantir alerts, and quest metrics to surface patterns and interactively recommend configuration improvements.
council
Use at the start of any non-trivial task. Loads focused, task-relevant context by reading CLAUDE.md, scanning for related files, and producing a structured Session Context block. Invoked automatically by quest or standalone via /council.