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/klimentij/klimkit/klimkit-workflownpx skills add klimentij/klimkit --skill klimkit-workflowgit clone --depth 1 https://github.com/klimentij/klimkitWrote 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/klimentij/klimkit/klimkit-workflow)<a href="https://agentmods.dev/skills/klimentij/klimkit/klimkit-workflow"><img src="https://agentmods.dev/badge/skills/klimentij/klimkit/klimkit-workflow.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.00441 |
| Opus 5 | $0.00032 | $0.00220 |
| Sonnet 5 | $0.00013 | $0.00088 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
klimkit-workflow 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 4d 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
Klimkit Workflow
Default Path
- Prefer the Codex app for day-to-day multi-machine interaction, remote follow-up, and live thread control.
- Use this plugin for reusable workflow guidance and skills.
- For Git-backed plugin updates, refresh the marketplace snapshot and re-add the plugin so the local cache moves to the new version.
- Use the full Klimkit repo-managed path only when the user needs machine orchestration, code-server profile projection, Switchboard, Tailscale Serve, Stop hooks, or home-level Codex config/subagent projection.
Workflow
- Read the repository instructions, relevant task notes, memory/log files, and nearby tests before editing.
- For implementation work, write an agent-authored acceptance checklist under
.klimkit/tasks/<feature>/. - Keep edits scoped to the request and reuse existing project conventions.
- Run verification that matches the blast radius. For UI work, include real screen evidence and a proof report under
.klimkit/reports/. - Record meaningful verification, skipped checks, and residual risk in a task proof note.
- For non-trivial work, append a timestamped reflection session to
.klimkit/reflection.mdafter verification and before final review. - Run final review before claiming completion. Use the repository's local instructions for the required reviewer count.
References
- Read
references/artifact-workflow.mdwhen the task needs concrete.klimkittask, proof, memory, log, report, or reflection layout. - Read
references/repo-managed-mode.mdwhen the user asks how the plugin differs fromkk apply, Switchboard, hooks, code-server, Tailscale Serve, or repo-managed Codex config.
Keep reference loading targeted. Do not load both references by default when the body above is enough.
What ships with it
3 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.
- 4d ago First seen · 31 lines · 64 tokens per session scan A 7d7683105495
klimkit-workflow is a skill published in the GitHub repository klimentij/klimkit (7 stars, last pushed 24d ago), licensed MIT. It adds 64 tokens to every session and 441 once invoked, about $0.0003 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
happiness-skill
当用户问「怎么才能更幸福/为什么得到了还不满足/怎么减少焦虑」时调用。 核心理念: 幸福是缺憾感清空的默认状态, 是可训练的技能; 欲望是与自己的契约(得到前不快乐), 同时只留一个重大欲望; 活在当下。 不适用于: 临床抑郁等需要专业治疗的场景(本书方法不能替代医疗)。 Triggers: 幸福/不快乐/欲望/焦虑/知足/活在当下/happiness/desire/anxiety.
agr-cli
Install, share, sync, and create AI agent skills across coding tools (Claude Code, Cursor, Codex, OpenCode, Copilot, Pi) using the agr CLI. Use whenever the user mentions agr, agr.toml, agr.lock, agrx, or asks to: add a skill ("install the pdf skill", "agr add ..."), sync agent resources across tools, share skills…
agr-release
Release process for the agr package. Handles version bumping (major/minor/patch/beta), changelog updates, pre-release quality checks, git tagging, and monitoring the GitHub Actions publish pipeline. Use this skill whenever the user wants to cut a release, bump the version, publish to PyPI, or asks about the release…
linkedin-post
You take a YouTube video transcript and produce a single LinkedIn post draft.
kafka-consumer-lag
Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress.…
mechanical-preprocess
Bash-first mechanical pattern cleanup pipeline — handles em-dash removal, forbidden words, and repetitive structures at scale BEFORE AI agents touch the text. Processes 30+ chapters efficiently.