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 satasuk03/kimi-plugin-cc --skill kimi-k2-promptinggit clone --depth 1 https://github.com/satasuk03/kimi-plugin-ccWrote 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/satasuk03/kimi-plugin-cc/kimi-k2-prompting)<a href="https://agentmods.dev/skills/satasuk03/kimi-plugin-cc/kimi-k2-prompting"><img src="https://agentmods.dev/badge/skills/satasuk03/kimi-plugin-cc/kimi-k2-prompting/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/satasuk03/kimi-plugin-cc/kimi-k2-prompting"><img src="https://agentmods.dev/badge/skills/satasuk03/kimi-plugin-cc/kimi-k2-prompting.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 | $0.00022 | $0.00368 |
| Opus 5 | $0.00011 | $0.00184 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
kimi-k2-prompting 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 11d 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.
What it actually says
Kimi K2.7 Prompting
Use this skill when tightening a delegated request into a strong prompt for Kimi K2.7 before calling task, reason, or review.
Kimi's coding model is strong at multi-step agentic coding and tool use, but it works best when the request is explicit and focused rather than open-ended.
Core rules:
- State the goal in one or two sentences before any supporting detail.
- Name the relevant files, directories, or modules instead of describing them vaguely.
- Call out constraints explicitly: languages or frameworks to use, things not to touch, style or testing requirements.
- Give clear acceptance criteria — what "done" looks like, including how it should be verified (tests, a command to run, a behavior to observe).
- Prefer one focused task per prompt. Split unrelated asks into separate
task/reasoncalls rather than bundling them. - For read-only work (
reason, investigation, review), say so explicitly so Kimi does not assume it should start editing. - For follow-up instructions on the same thread (
--resume-last), send only the delta instruction instead of restating the whole original prompt, unless the direction changed materially.
Prompt shape to aim for:
- Goal — what should exist or be true when this is done.
- Context — relevant files, directories, prior findings, or error messages.
- Constraints — anything Kimi must or must not do.
- Acceptance criteria — how to know the result is correct.
Keep the prompt compact. Extra scaffolding does not help K2.7 as much as a precise, unambiguous task statement.
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.
- 11d ago First seen · 28 lines · 22 tokens per session scan A a3332f3addbb
kimi-k2-prompting is a skill published in the GitHub repository satasuk03/kimi-plugin-cc (2 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 368 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
optimize
Rephrase a rough prompt to follow prompt-engineering best practices WITHOUT changing its meaning, then show it for review without executing. Use when the user runs /petprompt:optimize or asks to rewrite/clean up/optimize their prompt before running it.
nativeprompt
A prompt editor that adapts a user's request to the rules of a selected coding model, such as Claude Code, Codex, Gemini CLI, or GPT-5.
refine
Transform a brief or prompt into a structured, production-ready prompt via prompt-optimizer. File or text mode.
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.
goal-test
A local experiment for testing a goal command that keeps an AI coding session working until a stated condition is judged complete. It uses a separate language model to evaluate the conversation after each assistant turn.
ai-patterns
Invoke when building features that call LLM APIs, handling AI responses, managing prompts, or integrating AI SDKs. Contains error handling, security, prompt management, and observability patterns.