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 Wang-Cankun/cankun-skills --skill fleetgit clone --depth 1 https://github.com/Wang-Cankun/cankun-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/wang-cankun/cankun-skills/fleet)<a href="https://agentmods.dev/skills/wang-cankun/cankun-skills/fleet"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/fleet/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/wang-cankun/cankun-skills/fleet"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/fleet.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 | $0.00076 | $0.01704 |
| Opus 5.5 | $0.00030 | $0.00682 |
| Sonnet 5.5 | $0.00015 | $0.00341 |
| Haiku 4.5 | $0.00008 | $0.00170 |
Grade A, and why
fleet 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 yesterday.
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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fleet
Invoking this skill is the user's opt-in for the whole run, to multi-agent orchestration and to every model in the roster, as ultracode is for Claude-only workflows: author and run a Workflow for every substantive step, one per phase when the work has several.
Stages produce; this session accepts. Give stages the work that produces: surveying code, implementing, fixing, researching, hunting for problems, first reviews, drafting prose. Keep here the work that accepts: what the task is and what done means, and whether a result holds before anything relies on it (a merge, a reported number, a decision). How deep each acceptance goes is your judgement, from what a wrong acceptance would cost. UI look stays with Claude.
Read the user's crew preferences (~/.codex/agent-notes/crew.md, when present) before
writing the script: they set the coordinator's role, the flow of each unit and the
review policy, and win over this skill where they differ.
Choosing models
Read the user's model roster (~/.codex/agent-notes/models.md, when present) before writing
the script: which model does which kind of stage, its route and effort on this machine, and how
to steer it. Without a roster, choose from the models this machine's CLIs offer and tell the
user which. Put each stage's model, by full version, in its label (review:<model>-<effort>).
- Code from a brief you can settle up front, with checks that tell right from wrong, goes to the model the roster names for building to a brief. Code that needs judgement while it is written (a brief that cannot be settled, much of the codebase to weigh, the UI) stays with Claude; mechanical edits across many files go to a smaller Claude. A fix of a few lines, a merge conflict or the final integration you make here.
- A review whose wrong accept is expensive goes to a different family from the producer.
- A check of the whole run against its goal, and volume, go to the models the roster names for them.
- Findings from one family are claims until a check stage or this session rules on them, with
file:line; a second family's ruling is a vote, not the ruling. - A model the user names for the run gets the stage it fits best. Self-hosted models join only when the user enables one.
What ships with it
10 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.
- agents/openai.yaml 335 B
- references/lessons.md 5.2 KB
- references/routes.md 477 B
- runner/fleet-job/fleet_job.py 28 KB runs code
- runner/fleet-job/README.md 6.3 KB
- runner/fleet-job/submit_result.ts 1.9 KB runs code
- runner/fleet-job/test_fleet_job.py 22 KB runs code
- runner/fleet-runner.md 2.8 KB
- scripts/install-runner.sh 1.1 KB runs code
- scripts/usage.py 3.3 KB runs code
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.
- yesterday Changed · -3 lines 5422d74774d9
- 3d ago First seen · 109 lines · 76 tokens per session scan A f7162e989813
fleet is a skill published in the GitHub repository Wang-Cankun/cankun-skills (2 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 1,704 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 2026-10-29.
Other skills, from other repositories
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
webhook-management
Configure and validate CCAM webhook targets across supported chat, incident, automation, and generic providers. Use when listing provider requirements, creating or updating a target, scoping it to alert rules, sending a test notification, reviewing delivery history, or deleting a target.
gesellschaftsrechtliche-satzungen-agb
Für Gesellschaftsrechtliche Satzungen AGB Abgrenzung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: gesellschaftsrechtliche-satzungen-agb.
react-patterns
React 19 performance patterns and composition architecture for Vite + Cloudflare projects. 50+ rules ranked by impact — eliminating waterfalls, bundle optimisation, re-render prevention, composition over boolean props, server/client boundaries, and React 19 APIs. Use when writing, reviewing, or refactoring React…
design-review
Review a web app or page for visual design quality — layout, typography, spacing, colour, hierarchy, consistency, interaction patterns, and responsive behaviour. Not a UX audit (that checks usability) — this checks whether it looks professional and polished. Produces a design findings report with screenshots.…
render-airdrop-carousel
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…