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 Stanshy/AgentHub --skill sprint-proposalgit clone --depth 1 https://github.com/Stanshy/AgentHubWrote 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/stanshy/agenthub/sprint-proposal)<a href="https://agentmods.dev/skills/stanshy/agenthub/sprint-proposal"><img src="https://agentmods.dev/badge/skills/stanshy/agenthub/sprint-proposal.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 5.1 | $0.00019 | $0.00391 |
| Opus 5 | $0.00010 | $0.00196 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
sprint-proposal 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 7d 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
Sprint 提案書產生器
根據需求討論產出標準化的 Sprint 提案書。
使用方式
/sprint-proposal <sprint-number> <project-name>
參數
$0: Sprint 編號（如2）$1: 專案名稱（如AgentHub）
執行步驟
-
讀取提案書範本： 使用 Read tool 讀取
.knowledge/templates/sprint-proposal.md.template。 若檔案不存在，略過此步驟，改用預設格式。 -
讀取現有提案書參考格式： 使用 Glob tool 搜尋
proposal/sprint*-proposal.md，取最後一個（編號最大），用 Read tool 讀取前 30 行。 若無既有提案書，略過此步驟。 -
產出
proposal/sprint$0-proposal.md，包含：- 目標（1-2 句話）
- 範圍定義（做/不做）
- 流程決策（步驟勾選 + 關卡）
- 團隊分配
- 風險評估
- 驗收標準
- G0 審核區塊（空白待填）
產出格式
嚴格遵循 .knowledge/templates/sprint-proposal.md.template 的結構。
若範本不存在，遵循 proposal/ 下既有提案書的格式。
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.
- 7d ago First seen · 42 lines · 19 tokens per session scan A db07b6ddfa8b
sprint-proposal is a skill published in the GitHub repository Stanshy/AgentHub (201 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 391 once invoked, about $0.0001 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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issue-triage
3-phase issue backlog management with audit, deep analysis, and validated triage actions. Use when triaging GitHub issues, sorting bug reports, cleaning up stale tickets, or detecting duplicate issues. Args: 'all' to analyze all, issue numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit only.
cross-campaign
Discover and reference other camps, projects, and files across camp boundaries. Use when the user mentions another camp or campaign by name, references work done "in another project/camp", or needs to find/copy/compare code across camps.
check-cache-bugs
Audit Claude Code setup for cache bugs (CC#40524): sentinel, --resume/--continue, attribution header + ArkNill B3/B4/B5.
git-ai-archaeology
Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.
review-pr
Perform a comprehensive code review of a pull request.