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/bazaarnpx skills add simota/agent-skills --skill bazaargit 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 | $0.00047 | $0.05901 |
| Opus 5 | $0.00023 | $0.02950 |
| Sonnet 5 | $0.00009 | $0.01180 |
| Haiku 4.5 | $0.00005 | $0.00590 |
Grade A, and why
bazaar 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bazaar
"A landing page is one promise, one path, one decision. bazaar runs the studio that delivers all three."
End-to-end landing-page studio chain. bazaar writes no copy, designs no pixels, ships no code — it orchestrates the existing roster (Field → Cast → Pulse → Funnel → Vision → Muse → Artisan → Growth → Bolt → Judge → Launch) into a contracted, quality-gated pipeline from brief to shippable page.
It is the LP-axis sibling of atelier (design), titan (product build), and nexus (generic multi-domain) — the highest-converting LPs need coordinated research, strategy, copy, design, implementation, optimization, and launch, a chain no single agent owns.
Principles: One promise, one path, one decision · Conversion is the contract · Stage gates, not vibes · Borrow trust upstream, prove value downstream · Speed and clarity are the first UX.
What ships with it
9 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.
- ARCHIVED_2026-08-19.md 556 B
- reference/agent-roster.md 17 KB
- reference/autorun-schema.md 611 B
- reference/chain-recipes.md 9.9 KB
- reference/conversion-playbook.md 12 KB
- reference/craft-standards.md 22 KB
- reference/handoff-protocols.md 14 KB
- reference/ia-blueprint.md 21 KB
- reference/quality-gates.md 16 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 · 289 lines · 47 tokens per session scan A 33865dbdf9fe
bazaar is a skill published in the GitHub repository simota/agent-skills (75 stars, last pushed 9d ago), licensed MIT. It adds 47 tokens to every session and 5,901 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.