Map repositories into focused context packs
发布者
antreasantoniou 发布的技能
已发布 30 个技能,累计 22 次安装。
Design and compose multi-agent graphs for correctness, coverage, or creativity. Use when a task benefits from isolated proposals, explicit arbitration, adversarial verification, committees, recursive review, cross-modal checks, or saturation loops. Applies across agent runtimes; the included JavaScript workflow is one adapter.
Reconstruct missing project history safely
One secure Bitwarden unlock per 24 hours
Challenge ML claims with code and falsification
Design or audit self-describing ML run metadata attached at initialization, including intent, code and data identity, resume lineage, and evidence limits. Use when wiring a tracker, introducing a run naming pattern, or investigating why a run exists.
Turn conversations into grounded decisions
Govern and actively monitor collaborative agent work
Durable memory for projects and research
Run a test-sandwiched multi-agent build pipeline
Narrative-aware, approval-gated inbox triage
Preserve durable context across every Archivum
Propose a canonical home, concrete consumers, and any justified runtime home for a reusable tool, library, skill, document, or workflow. Use after building a reusable capability or when asked where it should live; execution requires approval of exact targets and actions.
Use GCP without recurring browser OAuth
Find relevant people and grow your professional network
Sync Claude and Codex project progress
Run a Byzantine 2+1 plan before consequential work and a fresh, plan-blind reality check afterward. Use when the user asks for Plus Ultra, maximum rigor, independent proposals, enforced verification, or help responding to a Plus Ultra hook gate. Claude Code can enforce the loop through the included hook adapter; on Codex and other hosts it is a convention unless separately wired and verified.
Evidence-backed browser and API test plans
Review author-written applications, essays, bios, proposals, and important correspondence through explicitly synthetic, contrasting review lenses while preserving the author's voice and final editorial authority. Use for substantive critique and author-led revision, not impersonation or automatic submission.
Prepare and verify a checkpoint-based handoff of agent work to another host or session while preserving context, permissions and single-writer ownership. Use when the user requests moving ongoing work or continuing after a local disconnect; this is not live process migration.
Design and visually review print-ready PDFs
Build your digital-twin writing voice
Design and configure self-contained recurring agent checks with explicit targets, authority, change-sensitive reporting and stop conditions. Use for requested monitoring, reminders or follow-ups; use the included Meta-Pulse guidance when reviewing existing watches.
Find and open locally running web interfaces
Explore a design or strategy decision through distinct straightforward, hybrid and creative approaches, with concrete tradeoffs and a reasoned recommendation. Use when the user wants options before choosing, not after they have already decided.
Track per-project Claude usage estimates and local GPU budgets, preview admission, atomically reserve capacity, and reconcile actual usage with a local dashboard.
Maintain an explicit Markdown commitments ledger with a local cockpit for NOW, next actions, deadlines, waiting items, workstream states and inbox capture.
Validate, plan and explicitly dispatch provenance-linked ML experiments using the bundled Python runtime, with budget reservations and durable result bundles.
Back up selected local Claude and Codex session stores additively into an explicitly chosen private destination, with a no-write preview.
Build a source-indexed navigation layer over a private Claude JSONL session, preserving evidence locations and uncertainty without reproducing private reasoning.
关于 Ottermind 技能
antreasantoniou 发布的全部技能。如果某位作者的思路本来就贴合你的工作方式,关注这个作者往往比一个个评估单独技能更省时间。
技能覆盖 Ottermind 智能体能做的全部工作:生成可编辑的 PowerPoint 演示文稿、产出产品图与品牌视觉、在你接入的机器上写代码并运行、把脚本或静态图变成视频、清洗并分析表格数据。技能的作用,就是把一个通用模型变成能稳定处理「你这一版」具体任务的工具。
技能就是一个带简短 frontmatter 头部的 Markdown 文件,所以你可以在运行前先读清楚它到底会做什么,按自己的规范改写,也可以分享给同事。一旦某个技能跑出了你要的结果,就能把它挂到智能体上或设成定时自动化,同样的活儿下次不用任何人再重写提示词。
- Ottermind 技能是什么?
- 一份用 Markdown 写的可复用指令包。它为某一类任务提供上下文、步骤和约束,让结果保持稳定,而不是取决于你当天提示词写得好不好。
- 怎么安装和使用技能?
- 打开任意技能的详情页,读完整份文档,然后安装到你的工作区。之后遇到匹配的任务,智能体可以自动调用它,你也可以直接按名字指定。
- 我可以自己写技能吗?
- 可以。技能就是一个 Markdown 文件,frontmatter 里写清楚它叫什么、什么时候该用。如果有个流程你总要反复向智能体解释,把它一次性写成技能,通常是让它变得可复用的最短路径。
- 技能要额外收费吗?
- 浏览和安装技能库里的技能本身不收费。运行技能与其他智能体任务一样消耗积分,具体取决于你选择的模型和任务的工作量。