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/echonpx skills add simota/agent-skills --skill echogit 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 | $0.00036 | $0.05717 |
| Opus 5 | $0.00018 | $0.02858 |
| Sonnet 5 | $0.00007 | $0.01143 |
| Haiku 4.5 | $0.00004 | $0.00572 |
Grade A, and why
echo 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 2d 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
Licensed MIT
The repository is licensed MIT, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
31 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
- reference/_common 13 B
- reference/analysis-frameworks.md 3.6 KB
- reference/autorun-schema.md 1.8 KB
- reference/cast 10 B
- reference/cognitive-persona-model.md 19 KB
- reference/collaboration-patterns.md 12 KB
- reference/council-mode.md 3.9 KB
- reference/demand-5whys-root-cause.md 9.9 KB
- reference/demand-calibration.md 2.5 KB
- reference/demand-handoffs.md 6.8 KB
- reference/demand-jtbd-switch-interview.md 10 KB
- reference/demand-llm-prompt-generation.md 6.4 KB
- reference/demand-mode-playbooks.md 6.9 KB
- reference/demand-opportunity-solution-tree.md 11 KB
- reference/demand-patterns.md 8.2 KB
- reference/demand-persona-embodiment.md 15 KB
- reference/demand-subcommand-behavior.md 8.2 KB
- reference/echo 10 B
- reference/heuristic-evaluation.md 6.7 KB
- reference/judge 11 B
- reference/output-templates.md 9.5 KB
- reference/process-workflows.md 12 KB
- reference/question-templates.md 12 KB
- reference/recipes-index.md 2.9 KB
- reference/sus-scoring.md 7.7 KB
- reference/think-aloud-protocol.md 7.8 KB
- reference/tri-engine-demand.md 21 KB
- reference/tri-engine-walkthrough.md 24 KB
- reference/ux-frameworks.md 11 KB
- reference/visual-review.md 17 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.
- 2d ago First seen · 289 lines · 36 tokens per session scan A 0b2aecdfc5de
echo is a skill published in the GitHub repository simota/agent-skills (75 stars, last pushed 9d ago), licensed MIT. It adds 36 tokens to every session and 5,717 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.