delegate-skills is a toolkit for assigning coding tasks to separate agent command-line tools while keeping control of review and commits. It is for developers who want to organize installed implementers into lanes for work such as features, tests, or UI, then delegate tasks by lane or directly. The catalogue entries define the setup and delegation workflows.
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 amElnagdy/delegate-skills --skill kimi-delegategit clone --depth 1 https://github.com/amElnagdy/delegate-skillsWrote 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/amelnagdy/delegate-skills/kimi-delegate)<a href="https://agentmods.dev/skills/amelnagdy/delegate-skills/kimi-delegate"><img src="https://agentmods.dev/badge/skills/amelnagdy/delegate-skills/kimi-delegate/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/amelnagdy/delegate-skills/kimi-delegate"><img src="https://agentmods.dev/badge/skills/amelnagdy/delegate-skills/kimi-delegate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00126 | $0.01502 |
| Opus 5 | $0.00063 | $0.00751 |
| Sonnet 5 | $0.00025 | $0.00300 |
| Haiku 4.5 | $0.00013 | $0.00150 |
Grade A, and why
kimi-delegate 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 9d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kimi Delegate
You are the orchestrator. Hand a bounded coding task to a separate implementer - the Kimi Code CLI - then review what it produced and land it yourself. You write the brief and own the judgment; Kimi does the typing in its own session; you verify and commit.
The loop needs only a shell command and file access, so any comparable orchestrator can drive it.
When NOT to use this
- The task is small enough to do inline; delegation overhead is not worth it.
- The
kimiCLI is not installed or authenticated. - You need a CLI-enforced read-only implementer. Headless Kimi has no read-only mode.
Prerequisites (check once)
- Install Kimi Code with
brew install kimi-codeon macOS/Linux, or use the native installer from the official Kimi Code documentation. - Authenticate with
kimi login(device-code flow, no TUI), or use/loginin the TUI. - Confirm
kimi --versionsucceeds. - Work in, or point
--cdat, the target git repository.
Choose the model alias
Kimi uses default_model from its config.toml when --model is omitted. To choose another model
alias, pass --model <alias from your kimi config>. Model aliases are user-defined config keys; use
one the human has configured rather than inventing one.
The loop
Run these five steps per task. Steps 1, 4, and 5 require judgment; 2 and 3 are mechanical.
1. Write the brief
Kimi sees only the text you send plus what it can inspect in the workspace - no chat history or shared context. Include the goal, current state, what to change, what to leave untouched, the project's actual gates, and a report contract. Tell Kimi not to commit. Keep one task per brief. See references/writing-the-brief.md.
2. Dispatch
Use the bundled helper. It wraps Kimi's headless prompt mode, captures the structured event stream,
and writes result.json. (<skill-dir> is the installed folder containing this SKILL.md.)
What ships with it
5 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.
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.
- 9d ago First seen · 124 lines · 126 tokens per session scan A becfead59a89
kimi-delegate is a skill published in the GitHub repository amElnagdy/delegate-skills (1,809 stars, last pushed 8d ago), licensed MIT. It adds 126 tokens to every session and 1,502 once invoked, about $0.0006 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.
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