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 galvani/kanban-pro --skill kanban-retrogit clone --depth 1 https://github.com/galvani/kanban-proWrote 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/galvani/kanban-pro/kanban-retro)<a href="https://agentmods.dev/skills/galvani/kanban-pro/kanban-retro"><img src="https://agentmods.dev/badge/skills/galvani/kanban-pro/kanban-retro/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/galvani/kanban-pro/kanban-retro"><img src="https://agentmods.dev/badge/skills/galvani/kanban-pro/kanban-retro.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 | $0.00200 | $0.06475 |
| Opus 5 | $0.00100 | $0.03238 |
| Sonnet 5 | $0.00040 | $0.01295 |
| Haiku 4.5 | $0.00020 | $0.00647 |
Grade A, and why
kanban-retro scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl` is an unrun check wearing the right label (AIR-2915, 2026-07-14). The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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 · 358 lines · 200 tokens per session scan A a1fc0f61448a
kanban-retro is a skill published in the GitHub repository galvani/kanban-pro (0 stars, last pushed 1mo ago), licensed AGPL-3.0. It adds 200 tokens to every session and 6,475 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
epic-lead
Use when delivering one approved Kanban EPIC through governed integration, review, and handoff.
teamlead
Use at the START of ANY code task — write/edit/refactor/read code, feature, bug-fix, migration, code investigation. The main thread acts as team-lead, NOT implementer — it delegates work to subagents and surfaces only decisions and results, keeping the main chat clean.
knbn
Alias for the kanban skill. Use when the user invokes or refers to Kanban as "knbn".
babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
agentrq
Execute tasks assigned by humans or supervisor agents within a specific AgentRQ workspace. Use when you receive channel messages or need to report progress on tasks.
anti-drift
Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.