Skills

Sidebar skill hub: browse and search DSH skills, import from a folder or GitHub, and read full SKILL.md bodies rendered as Markdown. Imports are copied into ~/.dsh/skills (existing names are overwritten only on opt-in), skills there can be moved to a .trash folder from the detail view, and GitHub imports download the repository archive from codeload.github.com. It uses the same package name (dsh-skill-hub) and mount id (skill-hub) as cheshireez/dsh-skill-hub and hoshino114/dsh-skill-hub, so only one of the three can be installed.

DSH 话匣子：往系统提示里注入一段常驻互动协议——边做边说、卡住了就开口、把提问讲清楚、验证有预算、说人话，让话少或爱自己扛的模型回到「边说边做」的节奏。默认对所有模型生效，可按需收窄。

Registers the video-studio skill in the DSH skill catalog. It builds a Chinese-narrated video from a script: edge-tts synthesizes the narration, the timeline is derived from each line's measured audio duration rather than estimated from text length, Pillow renders every frame deterministically and pipes the raw frames to ffmpeg as rawvideo, burned-in subtitles and SRT/ASS export come from that same timeline, physical-model sound effects and a generated score are mixed sample by sample with numpy, and QA runs in four layers: container, signal, temporal and page level (the page layer only when the browser engine is used). An optional Playwright-driven browser engine renders the whole film as Canvas2D instead, sending PNG frames through image2pipe. Needs network access for the narration, ffmpeg on PATH and Python with numpy, Pillow and edge-tts; the browser engine also needs Playwright with a local Edge/Chromium.

Experiment-orchestration SOP skill for long-running experiments. A top-level model drafts a decomposition plan — subtasks, the model for each, an acceptance figure with tolerance, and dependency order — and dispatches only after the user confirms that plan; each subtask then goes to a lightweight executor subagent as a self-contained prompt, and its report is confined to measured facts and the delta from the agreed figure, with judgement and decisions staying at the top level. Ships one plan template and five task templates (gather, execute, consistency check, read-only review, mechanical transform), acceptance gates split into a task layer and an experiment layer, and three rules that stop internal-consistency evidence from being presented as independent or external validation.

Agent skills for the Lark/Feishu CLI: exports the 27 skills embedded in the lark-cli binary (docs, sheets, base, calendar, drive, task, wiki, approval, mail, slides, vc, minutes and more) and registers them with the DeepSeek Harness skill registry so the agent can read them on demand. Excludes lark-im so it complements an IM bridge plugin rather than duplicating one. Requires lark-cli to be installed.

DeepSeek Harness port of the Impeccable frontend-design skill: 24 design playbooks covering shaping, critique, audit, polish, layout, typography, motion, accessibility and design-system documentation, driven by the reference files shipped in the skill plus PRODUCT.md / DESIGN.md in the project instead of a downloaded engine binary. Registers the impeccable skill; reads project files and writes design artifacts under .impeccable/. The binary-only features (live browser bridge, pixel measurement, font matching) fall back to static variants, screenshots and explicitly declared manual estimates. Ported from pbakaus/impeccable (Apache-2.0).

Registers a frontend-design skill that asks for a design plan and token system before writing code, and ships Chinese typography rules plus a measured no-screenshot self-check. It provides one skill and no tools, makes no network requests and writes no files.

E-commerce order analysis skill for DSH: refund-type classification, return-rate statistics, interception and refund-abuse loss accounting, after-sales analysis with product warnings, delivered as a single Excel workbook with a per-order detail sheet and a sampling verification sheet (the agent builds it with Python: pandas, openpyxl, matplotlib).

Auto-detects whichever memory tool local Hermes uses, then reuses its skills and memory in DeepSeek Harness, with always-available recall. Needs python3 and a local Hermes installation; it only reads from Hermes.

Skill manager with content-addressed version history: snapshots, per-file and line-level diff, rollback, zip export and import, and per-skill enable or disable.

Provides a PMNote skill for finding public resources on game project management, careers, and AI tools.

Installs a TeleAgent skill-authoring standard as a DSH skill: the full spec, a validator that reproduces the platform checks (and fails on .pyc bytecode), a scaffolder, a packer and a TeleAgent installer.

Guide an agent through exporting selected OneLap cycling FIT files, validating data and coordinates, and uploading via a logged-in Strava browser session. Needs Python 3.9+ (standard library only) and an agent with browser and file-upload tools.

Ships the Alibaba Java coding guidelines, 44 selected rules, as an embedded harness skill, so writing, changing and reviewing Java code or DDL follows the handbook with a numbered self-check before delivery.

Matches each new task against installed skills with deterministic keyword scoring and injects the best one without being asked; records effective operations as per-skill notes for later runs.

Intervals.icu-only endurance-coaching skill bundle that turns Intervals.icu training data (activities, intervals, streams, training load CTL/ATL/TSB/rampRate, HRV, resting heart rate, sleep in-bed hours, eFTP, power curves) into concrete training decisions: readiness gating, VDOT pace and heart-rate zones, periodized weekly plans written back to the Intervals.icu calendar as events after confirmation, session reviews, race-day pacing and fueling, and weather-adjusted workouts. The bundle registers the endurance-coach skill in the profile, ships deterministic helper scripts, reads an athlete profile created by its init script, and requires the agent to state when data is unavailable instead of inventing it. It needs a separately installed Intervals.icu MCP server for training data and Python 3.10+ for its bundled scripts; with neither it works only from numbers you type in.

Career knowledge base vault with ten agent skills for setup, capture and import, audit, JD resumes, interview prep and review, plus Feishu interop.

Moves skills into a configurable directory and lets each workspace choose which skills it enables, avoiding context spent on unused skills.

Windows host rules for DSH agents: one skill with nine individually switchable rules (UTF-8/BOM, CRLF-LF per file type, build output, SmartScreen, _scratch isolation, screenshot privacy gate, teardown, shared-doc lock, local trust marks) and six bundled dependency-free scripts.

An on-demand Chinese editing skill that reduces formulaic phrasing while preserving facts, uncertainty, genre, and author voice.

Reusable experience for DeepSeek Harness: save a successful task as a structured playbook, recall it later as a native skill, and gate deliveries behind user-confirmed checks that require objective evidence before write actions are allowed.

Manage local skills across AI agents with version comparison, shared-directory consolidation, and recoverable migration and deletion.

Autonomous product-polish loop skill: every round opens a fresh brainstorm from 3 forced perspectives, picks one small angle capped at ≤3 files and ≈100 lines, implements it, verifies with the repo's own tests, build or lint, and commits it as a single round commit; the round is logged to a per-project ledger, and ideas too big for one round go to an icebox. A red check ships nothing — the round commit is reverted immediately so the failed diff stays in history. Non-git targets are rejected before any work starts. Pure skill bundle: no MCP server, no runtime dependencies. Requires DSH 0.2.1-rc.2 or newer, below 0.3.0.