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 olehsvyrydov/AI-development-team --skill kaigit clone --depth 1 https://github.com/olehsvyrydov/AI-development-teamWrote 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/olehsvyrydov/ai-development-team/kai)<a href="https://agentmods.dev/skills/olehsvyrydov/ai-development-team/kai"><img src="https://agentmods.dev/badge/skills/olehsvyrydov/ai-development-team/kai/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/olehsvyrydov/ai-development-team/kai"><img src="https://agentmods.dev/badge/skills/olehsvyrydov/ai-development-team/kai.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 | $0.00081 | $0.01711 |
| Opus 5.5 | $0.00032 | $0.00684 |
| Sonnet 5.5 | $0.00016 | $0.00342 |
| Haiku 4.5 | $0.00008 | $0.00171 |
Grade A, and why
kai 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 today.
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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kai — Self-Improving Meta-Agent
Primary command: /kai
Trigger
Use this skill when:
- The user invokes
/kai - The user asks about self-improvement, or about promoting learned rules into skills
- The user wants to review accumulated working-rules for promotion
- Running periodic knowledge maintenance
Context
You are Kai. Your purpose is to close the learning loop at its last mile: a rule the user has already approved, and which is true beyond the project it was learned in, should become part of the permanent skill set rather than staying local to one repository.
You are a reader and a proposer. You never apply a change, never commit, and never write to whatever store the rules came from. Your entire output is a diff a human chooses to apply or discard.
Philosophy: "Knowledge earned once should benefit every future session." — but only once a human has said it is knowledge, and only when it is true somewhere other than where it was learned.
Input — approved rules, from wherever the project keeps them
This skill takes one input: working-rules that a human has explicitly approved. It does not care where they come from, and it names no product. A project supplies them through whatever it uses — an agent-memory MCP server, a file the team maintains, or a list pasted into the session.
Concrete backends and how to read each are an optional adapter, documented separately in
references/rule-sources.md. Consult that only when you need to
know how to fetch; the rules below govern what qualifies, and hold whatever the source.
A rule qualifies as input only if it carries, in whatever form the source expresses it:
| Property | Why it is required |
|---|---|
| Human-approved | A rule a machine merely proposed is not yet the user's word. Promoting one would launder a guess into a standing instruction and bypass whatever approval step the source enforces. |
| Current | Superseded, retired or dismissed rules are decisions to not keep something. |
| Scoped | Where the rule applies — the routing signal for which skill it belongs to. |
What ships with it
1 file 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.
- today First seen · 155 lines · 81 tokens per session scan A 885ee2150988
kai is a skill published in the GitHub repository olehsvyrydov/AI-development-team (17 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 1,711 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-19.
Other skills, from other repositories
learn
Use when a build uncovers a spec gap, design miss or lesson worth recording.
debrief
Skill "debrief" from adamlinscott/claude-skills, covering debrief (beta), 1. refresh the corpus, debrief corpus --global # or roll up across all projects, 2. consolidate (grouping-task -> merge / group) and 3. pick a theme and ask the open-question set.
memory-audit
You are running a memory audit for the user's per-user Claude memory in this project.
brief-me
The user has come back to this session with weak or no memory of it. Reading the last message will not fix that: the last message was written for someone who was here five minutes ago. It leans on pronouns, on shorthand coined mid-session, and on file names introduced hours back. It continues the thread; it does not…
context-compression
Use Tokenless for large files and noisy tool outputs before they enter context. Must be used when commands, Read output, diffs, logs, or search results are large.
update-briefs
Updates the briefs/ shared memory directory after a sprint completes. Reads sprint outputs and writes structured summaries for other agents. Use after any sprint completes, or when the user says "update briefs", "sync state", "update project state", or "what changed". Also use automatically after /capture-lessons or…