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WhaleKit

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Shell语言
2026-08-14最近推送
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功能简介

为DeepSeek Harness定制的超能力

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我们的评价
可用 — 实测通过,早期项目

为DeepSeek Harness定制的超能力 实测能干净安装、正常启动。早期项目,但功能可用。

「已验证」表示我们的自动化 CI 在干净 profile 里实际执行了 dsh plugin add 并启动成功——仅此而已。功能描述与版本兼容性均为作者声明。这不是安全审计,也不代表对第三方代码的背书。

README

WhaleKit

WhaleKit is an agentic skills framework for DeepSeek Harness (DSH). It is a collection of
composable, DSH-native SKILL.md skills plus an entry skill that ensures agents use them — built for
full autonomy from upstream roadmaps and hardened against hallucination with an adversarial review
mechanism.

Together with install.sh, it replaces binary skill invocation (Superpowers' weakness) with a
five-tier routing ceremony: process depth is chosen per task, from none at L1 up to full ceremony
at L5. Git is the traceability layer; every authoritative document is amendable but every amendment
is a commit.

Quick Start

1. Install

From the repository root, run the installer:

./install.sh                 # install into ~/.dsh/skills (DSH_HOME overrides the root)
./install.sh --project       # install into <project>/.dsh/skills instead
./install.sh --uninstall     # remove every WhaleKit skill symlink (works with --project too)
  • Default: symlinks all 22 skills into $DSH_HOME/skills (defaults to ~/.dsh/skills).
  • --project: symlinks into <projectRoot>/.dsh/skills, which DSH ranks above the home root.
  • --uninstall: removes every WhaleKit symlink and the emptied target directory.
  • Options are order-independent (install.sh --uninstall --project behaves identically).

DSH discovers skills from <projectRoot>/.dsh/skills (rank 100) before ~/.dsh/skills (rank 400),
so a project-local install takes precedence over a global one.

2. First session

using-whalekit is the entry skill: it must be invoked at the start of any conversation. After
using-whalekit, invoke whalekit-conventions (mandatory) — the canonical global-conventions skill
that binds every agent, including subagents, before any task work begins. When you begin a task,
WhaleKit will classify it (trivial vs non-trivial), run targeted-exploration if needed, recommend a
routing tier, and present 2–3 tier options for you to choose. The user's decision is the highest
authority below the system prompt — the agent never picks the tier alone.

The Five Tiers

WhaleKit replaces binary skill invocation with a graduated routing menu. Process depth is chosen
per task, from none at L1 up to full ceremony at L5. Core flow:

Session start → using-whalekit (mandatory entry)
  → Task assessment: is exploration needed?
      ├─ Trivial task (one-line fix) → artifact chain at Q1
      └─ Non-trivial task → targeted-exploration (target = task core or whole project)
           → Exploration report: findings + evidence + risks + scope estimate
  → Main agent applies routing: asks the artifact chain one question at a time
     (Q1 memo? → Q2 spec? → Q3 plan? → Q4 dual?)
  → User answers each question (gold standard)
  → Skills gated by reached tier → execution
Tier Process Trigger signals (from exploration report)
L1 Immediate implementation No process Purely mechanical change, minimal risk
L2 Memo only Memo persisted to disk Small feature, single file
L3 Memo → Spec + specification Medium feature, touches interfaces
L4 Memo → Spec → Plan + implementation plan Large feature, multiple files/modules
L5 Full + dual records New project, architecture-level, directional decisions

The tier is reached one question at a time via the artifact chain — the tier = how far the chain
went (memo → spec → plan → dual records, one question at a time): a "no" at Q1 stops at L1,
memo-only at L2, memo + spec at L3, memo + spec + plan at L4, and dual records at L5.

Iron rules of routing:

  1. User is the gold standard. Any tier recommendation must present 2–3 options; the user chooses.
    The agent never decides the tier alone.
  2. One-way ratchet. Hidden complexity discovered mid-task upgrades the tier — stop, say so, step
    up. Nothing downgrades mid-task.
  3. Exploration before recommendation. Tier recommendations for non-trivial tasks must be based on
    a targeted-exploration report, never on impression.

Skill Inventory

22 skills (9 original + 13 adapted). Adapted skills retain their
Adapted from obra/superpowers (MIT) header; see individual SKILL.md headers for attribution.

Skill Source
using-whalekit original — entry skill, routing startup
whalekit-conventions original — canonical global conventions binding all agents
targeted-exploration original — subagent-driven anti-divergence exploration feeding routing
routing original — five tiers, gating, one-way ratchet
adversarial-review original — red/blue/black meta-skill (decision + review modes)
dual-records original — DEVELOPMENT.md + README.md (amendable truth)
commit original — commit strategy gate + logical commit procedure
clarification-questioner original — question-asking methodology: generate → curate → ask one at a time → converge answers into a verified conclusion sheet (clarity/quality checked) before design
decision-approval original — waived decisions become a draft sheet (proposal/rationale/alternatives/confidence) the user approves or edits before they take effect
socratic-brainstorming adapted (heavy) — from obra/superpowers brainstorming
test-driven-development adapted — from obra/superpowers
systematic-debugging adapted — from obra/superpowers
verification-before-completion adapted — from obra/superpowers
requesting-code-review adapted — from obra/superpowers
receiving-code-review adapted — from obra/superpowers
using-git-worktrees adapted — from obra/superpowers
finishing-a-development-branch adapted — from obra/superpowers
writing-plans adapted — from obra/superpowers
executing-plans adapted — from obra/superpowers
subagent-driven-development adapted — from obra/superpowers
writing-skills adapted — from obra/superpowers (skill TDD)
dispatching-agents adapted — from obra/superpowers (generic delegation protocol; re-scoped 2026-08-14 from the parked parallel clone)

Design Philosophy

  • User is the gold standard. The user's decision is the highest authority below the system
    prompt. Every major decision presents 2–3 options; the agent never decides alone.
  • Ceremony scales with task. Process depth is graduated (5 tiers) and chosen per task; small
    tasks get small process. The direct answer to Superpowers' binary-invocation problem.
  • Fight hallucination with adversarial review. Big decisions and architecture-level bugs go
    through red/blue/black subagent loops (decision mode) and review-mode red-finds/blue-fixes loops,
    not single-agent judgment.
  • Amendable truth. Review baselines (README.md in dual records) are authoritative but explicitly
    amendable, with every amendment traced in git.

All documents form a golden-standard chain — Dual → Plan → Spec → Memo → User requirement — where
the user requirement is the highest authority and live user decisions outrank the chain. Process
artifacts enter git by consent: every artifact write is preceded by a commit-policy check.

Installation

A single install.sh symlinks the skills into the DSH root, with --project (project-local
.dsh/skills) and --uninstall modes. See Quick Start.

Testing

Skill TDD via pressure scenarios in tests/pressure/<skill>/ — each skill has ≥2 scenarios (one
skip-temptation, one misleading-execution) plus pass-criteria.md. Run the harness:

tests/run.sh <skill>          # print the scenario prompt and pass criteria
tests/run.sh <skill> --dispatch  # print a ready-to-use subagent dispatch prompt
tests/run.sh <skill> --live      # print a manual live-session verification checklist

--live exists for entry-sensitive skills (whose ceremony a dispatched subagent is correctly
exempted from by a <SUBAGENT-STOP> block) and any skill requiring live user interaction.

Roadmap

  • v0.1 (current): DSH-only, coding domain, 22 skills, five-tier routing.
  • v0.2 (planned — 2026-08-14 decision): multi-runtime planning. DSH-only is a concentration risk (SuperPowers ports to many harnesses); evaluate adapting the skill set to other harnesses to reduce single-runtime exposure. Scope and timeline TBD by a spec-level decision.

License

MIT. Portions adapted from obra/superpowers (MIT);
see individual SKILL.md headers for attribution.

安装

🧩 让 Agent 自动装(推荐)

装一次目录插件,之后本站所有插件都能让 DeepSeek Harness 自动找、自动装:

dsh plugin add dshbase-catalog

然后对 agent 说「帮我装 WhaleKit」,它会在目录里找到并自动安装。文档:dshbase-catalog · 已验证场景包。

该插件是 GitHub 源码(未发 npm)——直接从仓库装:

Web profile:

dsh plugin --profile web add github:zprolab/WhaleKit

Headless(CLI)profile:

dsh plugin --profile headless add github:zprolab/WhaleKit

实测报告

验证通过:从 GitHub 源码完成 L1 安装 + L2 加载 + L3 运行(dsh 0.1.0-rc.6)。

使用场景

改变 dsh 的外观或交互方式——一套主题、皮肤或新面板,重塑工作区。

适合谁

在 web UI 里一待几小时、想让它按自己的习惯好看又好用的人。

二次开发建议

皮肤、面板和主题 token 是扩展点——写新皮肤、加面板,或与上游配色同步 token。

安全:尚未扫描——我们的每日静态扫描将很快覆盖它。

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