Iván
作者:Iván更新日 2026-05-11kimi@clawhub_ivangdavila/kimi
ワークフロー編成ツール呼び出し#Coding#Debug

AlphaMoatによると、2026-09時点でKimiのダウンロード数は1,881、スター数は0で、AIスキル全体5,078件中4,513位、AIエージェントでは969件中892位です。

インストール数
218
ダウンロード数
1881
スター数
-
総合順位
#4,513
全体の上位 89% · AIエージェントの上位 93%
プラットフォームインストール数ダウンロード数スター数コメント
ClawHub ↗341234--
SkillHub ↗2181881-—

プラットフォームごとに集計方法と集計時点が異なります。

このスキルでできること

公式 Moonshot API で認証と現在のモデル一覧を確認し、用途別にリクエスト、再試行、解析、互換クライアントの移行を整理します。構造化出力は検証や追加の正規化後に後続処理へ渡します。機微な資料は匿名化し、送信前に明示的な同意を得ます。既定では状態を保存せず、利用者が継続利用を望んだ後にだけノートやログを保存します。

資料公開日 2026-10-26

できること

  • Kimi のリクエストや既存クライアントの移行を調べる

使う場面

  • 認証、モデル名、解析、移行で問題があるとき

プロンプト例

スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。

この Kimi リクエストの接続先、モデル選択、解析処理を確認してください

利用条件

スキルのドキュメントの記載に基づいて整理したもので、実際のインストールや動作は検証していません。

認証に関する注意
MOONSHOT_API_KEY は環境変数で使用し、値をノートへ書きません。
設定の要件
  • 最小の接続確認には curl と jq が必要です。

月次の推移

ダウンロード数1881
297114819982026-03 · ダウンロード数 4142026-05 · ダウンロード数 8402026-06 · ダウンロード数 9362026-07 · ダウンロード数 11592026-08 · ダウンロード数 14152026-09 · ダウンロード数 188126-0326-09
スター数-
0112026-03 · スター数 -2026-05 · スター数 -2026-06 · スター数 -2026-07 · スター数 -2026-08 · スター数 -2026-09 · スター数 -26-0326-09

2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。

月ダウンロード数前月比スター数
2026-091881—-
2026-081415+22.1%-
2026-071159+23.8%-
2026-06936+11.4%-
2026-05840—-
2026-03414—-

SKILL.md 原文

作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。

6.6 KB・ClawHub v1.0.0
---
name: Kimi
slug: kimi
version: 1.0.0
homepage: https://clawic.com/skills/kimi
description: Build and debug Kimi API workflows for chat, coding, reasoning, and tool-calling with live model checks, retries, and safe routing.
changelog: Initial release with Kimi workflow routing, OpenAI-compatible request patterns, migration guidance, and operational safety checks.
metadata: {"clawdbot":{"emoji":"🌙","requires":{"bins":["curl","jq"],"env":["MOONSHOT_API_KEY"]},"os":["linux","darwin","win32"],"configPaths":["~/kimi/"]}}
---
すべて表示(全 145 行)
## When to Use

User needs Kimi to work reliably for chat, coding, long-context research, structured outputs, or agent workflows. Agent handles live model verification, request shaping, migration from other OpenAI-compatible providers, and failure recovery before the workflow is trusted.

## Architecture

Memory lives in `~/kimi/`. If `~/kimi/` does not exist, run `setup.md`. See `memory-template.md` for structure.

```text
~/kimi/
├── memory.md         # Status, activation rules, and stable defaults
├── routes.md         # Preferred route per workload
├── approvals.md      # Sensitive-send boundaries and redaction preferences
├── experiments.md    # Prompt, parser, and fallback notes
└── logs/             # Optional sanitized repro payloads
```

## Quick Reference

Use the smallest file that resolves the blocker.

| Topic | File |
|-------|------|
| Setup process | `setup.md` |
| Memory template | `memory-template.md` |
| Minimal request patterns | `api-patterns.md` |
| Workload routing choices | `routing-matrix.md` |
| OpenAI-compatible migration | `migration-playbook.md` |
| Trust and redaction workflows | `safety-workflows.md` |
| Fast diagnosis and recovery | `troubleshooting.md` |

## Requirements

- `curl` and `jq` for minimal endpoint checks
- `MOONSHOT_API_KEY` kept in environment variables only
- Kimi access through the official Moonshot API base URL
- User approval before persisting local notes or sanitized logs

## Core Rules

### 1. Verify Auth and Live Models Before Naming Any Route
- Start with `https://api.moonshot.ai/v1/models` and copy live model IDs from the response.
- Never trust remembered Kimi model names, screenshots, or stale blog examples when a workflow is failing now.

### 2. Lock the Job to One Workload Before Tuning Prompts
- Classify the request as one of: fast chat, coding agent, long-context research, deterministic JSON, or migration debugging.
- Most bad Kimi advice comes from mixing several jobs into one oversized prompt and then blaming the model.

### 3. Treat Structured Output as a Separate Reliability Path
- If output feeds tools, code execution, or downstream writes, use strict schemas or a second normalization pass.
- Do not ask one response to do open-ended reasoning and perfect machine-readable output at the same time.

### 4. Keep Sensitive Data Out Unless the User Explicitly Approves It
- Redact secrets, customer identifiers, internal hostnames, and raw tokens before sending prompts externally.
- If the user wants repeatable Kimi workflows, save the redaction rule and approval boundary in `~/kimi/approvals.md` after confirming the first write.

### 5. Route by Deadline and Cost, Not Brand Habit
- Use the smallest Kimi route that can finish the current job reliably.
- For recurring workflows, save one primary route and one fallback route instead of debating models from scratch each time.

### 6. Separate Provider Migration Problems From Model Problems
- When moving from OpenAI-compatible code to Kimi, isolate the variable: base URL, auth env var, model ID, parser, or retry policy.
- Reproduce with one minimal payload before changing prompts, infrastructure, and business logic together.

### 7. Ask Before Creating Persistent State
- Work statelessly by default.
- Only create `~/kimi/` notes, approvals, or debug logs after the user wants continuity across Kimi tasks.

## Common Traps

- Hardcoding a remembered model ID -> fetch `/models` and use the live ID instead.
- Treating Kimi as one generic route -> split coding, reasoning, JSON, and migration work.
- Sending raw internal logs to the API -> redact first and preview what leaves the machine.
- Combining creative reasoning with strict JSON output -> use a second deterministic pass.
- Blaming the model for every failure -> verify auth, base URL, retries, and parser behavior first.

## External Endpoints

Use only the official Moonshot API surface required for the current task.

| Endpoint | Data Sent | Purpose |
|----------|-----------|---------|
| https://api.moonshot.ai/v1/models | Auth header only | Discover live Kimi models |
| https://api.moonshot.ai/v1/chat/completions | Prompt messages and options | Kimi chat, reasoning, coding, and structured-output requests |

No other data is sent externally.

## Security & Privacy

**Data that leaves your machine:**
- Prompt content sent to the Moonshot API when the user asks for Kimi inference
- Optional sanitized excerpts of code, logs, or documents sent for analysis after approval

**Data that stays local:**
- Activation preferences, route defaults, and approval boundaries in `~/kimi/` after user approval
- Optional sanitized repro payloads and troubleshooting notes saved for recurring workflows

**This skill does NOT:**
- Store `MOONSHOT_API_KEY` in markdown or project files
- Send data to undeclared endpoints
- Persist raw secrets or sensitive prompts without explicit user approval
- Modify its own skill files

## Scope

This skill ONLY:
- designs and debugs Kimi API workflows
- routes Kimi usage across coding, reasoning, research, and deterministic-output jobs
- hardens retries, validation, and migration from other OpenAI-compatible providers
- stores lightweight local notes only after user approval

This skill NEVER:
- invent live model availability without checking
- persist secrets in `~/kimi/`
- execute destructive downstream automation from unvalidated output
- treat cost-sensitive or sensitive-send boundaries as implicit

## Trust

Using this skill sends prompt data to Moonshot's Kimi API.
Only install if you trust Moonshot with that data, or keep sensitive preprocessing local and sanitized.

## Related Skills
Install with `clawhub install <slug>` if user confirms:
- `api` — debug auth, payloads, retries, and OpenAI-compatible request shapes
- `models` — compare model families and cost tiers before locking Kimi into production
- `coding` — tighten coding-agent behavior after the Kimi route itself is stable
- `backend` — connect Kimi workflows to services, jobs, and API boundaries
- `fastapi` — expose Kimi-backed endpoints with request validation and safer deployment defaults

## Feedback

- If useful: `clawhub star kimi`
- Stay updated: `clawhub sync`

AIエージェントの人気スキル

self-improving agentAIエージェント

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude rea

↓ 129万★ 4521pskoett
Find SkillsAIエージェント

场景驱动+关键词双模式技能发现工具。当用户用自然语言描述场景/需求(如"我想做一个海报""帮我分析股票"),或明确说"安装技能/find skills/找个skill"时,自动从官方内置、本地已安装、SkillHub、虾评、GitHub、ClawHub 六层联合搜索并推荐最合适的技能,支持一键安装。已完全替代官方...

↓ 110.3万★ 1778桂皮
Agent BrowserAIエージェント

AI エージェント向けのブラウザ自動化コマンドラインスキルです。

↓ 90.9万★ 991Nicolas Kleiber
Self-Improving + Proactive AgentAIエージェント

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...

↓ 44.4万★ 1606Iván
Skill VetterAIエージェント

第三者のスキルを入れる前に、出所と権限と危険な作りを手順に沿って調べるスキルです。

↓ 33万★ 1323spclaudehome
ontologyAIエージェント

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...

↓ 29.4万★ 788oswalpalash
Proactive AgentAIエージェント

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞

↓ 28.8万★ 913halthelobster
Skill CreatorAIエージェント

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

↓ 23.8万★ 473chindden

Ivánのその他のスキル

データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。