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Nai milk-frog mascot appearance layer for the DeepSeek Harness web GUI: wears the bundled milk-frog artwork as the GUI background (surface-token fade with opacity, blur, scrim and position controls), seats the matching square avatar as the sidebar and conversation brand mark, and edits it all from a Settings section; the host serves the artwork and config locally under /api/nai, and the config lives at $DSH_HOME/nai.json.

Windows용 애니메이션 동반자입니다. DeepSeek 잔액/Codex 할당량, Codex 다중 계정 OAuth, 계정별 할당량/모델 관리, 컴팩트 모드와 설정 자동 저장을 지원합니다. 패널 계정 순환은 선택 사항입니다. 일일 warm-up은 별도 명시적 허가가 필요합니다. 다중 계정 startup/5h-full 트리거는 아직 제공되지 않습니다.

Detect DSH plugin conflicts and loading failures, quarantine affected bundles and recover them, with offline startup rescue.

Adds a Hindsight configuration and status dashboard to the DSH sidebar for connection, model, embedding and bank settings, with health checks and ownership-checked local service controls; memory tools are provided by the separate official integration. Hindsight itself is a separate service and is not installed by this plugin.

Exposes the Paper-gogo evidence-first paper workflow as native tools: all 18 phases and the seven G0-G6 gates become callable, gate results are normalised and advancement is refused while any gate fails, and claims are audited against the four evidence states SUPPORTED, INFERRED, VERIFY and MISSING. Ships the workflow text and 57 skills in its own directory, so it installs without a separate Paper-gogo package.

Pops the DSH side chat into a frameless, always-on-top Windows desktop window that can sit over any other application, with its own settings page for appearance, pinning, remembered geometry, opacity, ephemeral threads and cache cleanup.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page whose left column switches between the framework, the evidence, validation, evaluation, step evidence, run logic, the before/after of a modification when there is one, the definition facts and the raw SKILL.md. Declaration and run stay apart: the body is split into structure, declared workflow and progressive disclosure, the run logic is read from local receipts, and every declared step carries its own evidence — no observed evidence is reported as exactly that, not as executed or not executed, and the run's instruction hash sits beside the current definition as match, mismatch or unavailable. The evidence view gives each subject one of three states (declared, observed, unavailable), walks definition → load → use → outcome, names what each stage cannot reach, declines to attribute an outcome to the Skill, and can copy the snapshot out as byte-stable JSON. Validation checks the Agent Skills open standard and the platform profiles separately, each finding on a rule id and labelled standard compliance or platform compatibility. Evaluation keeps repeatable cases with the conditions each run was recorded under and the four stages of runtime evidence, comparing two runs of one case item by item without scoring them. The trigger boundary keeps should-trigger and should-not-trigger prompts, has you run them in a real new session, then records what was observed — whether a load record appeared and whether it was model-invoked or user-explicit; a typed /name is not a natural trigger, and an absent load record is reported as not observed, not as not triggered. The same projections are exposed to the agent as a read-only skill_review tool, which returns a structured review packet and says what it omitted when the packet will not fit whole. The inspector, the evidence chain and these reviews are computed on this machine from the definition and the session receipts; the Chinese reading version sends the body through the host's model when you ask for it, keeping code fences, paths and frontmatter verbatim and storing the result on this machine. A Skill can also be cloned into a local copy. It does not write back to the source Skill and does not run its scripts.

Cut LLM costs: a Jev (System One) classifier vets each turn against a declarative policy and recommends the subagent model route, so routine work runs at flash rates while the main model keeps the hard parts; explicit /route trigger, runtime delegation-capability gate, credential redaction, and offline policy replay over the verdict log.

DeepSeek Harness plugin that fixes RTL/LTR bidi rendering in chat: per-block direction for Persian and Arabic messages with embedded English, plus list markers and quote bars that follow their text

AMI MegaRAC OneTree 一键日志（OpenBMC dreport dump）上传与分析框架：设置面板「OneTree 日志」上传压缩包或登记服务器本地路径，安全解包、自动探测 dump 根、文件清单分类，为第二阶段逐域解析提供内核。

Enhanced desktop shell for DSH: auto-starts the local backend on launch (no browser tab), asks before closing the backend on quit, and starts the backend in parallel with Electron for faster cold start. Fork of dsh-clean-desktop-shell.

A dsh-better-sidebar reading view for .docx files: headings from the style map, paragraphs, lists, real table cells, inline pictures and header notes. Ceilings on archive size, per-part inflated bytes and rendered blocks keep a document from freezing the tab.

macOS desktop launcher with an installation skill: starts or reuses the local DeepSeek Harness web service and opens it in Google Chrome, with a transparent character and thought bubble icon.

Folds long composer drafts into a compact summary card once a character threshold is reached, without changing message submission.

DSH plugin that registers the ai-eight-honors-eight-shames skill: eight checkable working rules for coding agents (verify before calling, confirm ambiguity, cite evidence, reuse before creating, test before claiming done, follow local conventions, separate fact from inference, keep changes reversible) plus a ninth rule that logs each user request and the change made in two fixed report files at the workspace root.

Task-to-skill pairing meta-skill with a laziness ladder for minimal loading, a user-editable routing table, and a quarantine, SkillSpector scan, human-approval install flow.

Media-scape theme for the DSH web UI: standing/character-art and video wallpapers (mp4/webm, local + online) with boot animation, base color schemes (single-source JSON + presets + picker), capsule host-element styling, ambient particles, background music player (covers/playlists/random), typing sound effects, and a draggable dock toolbar with a unified upload entry — wallpapers and music uploaded together (suffix-classified), mid-upload no-disk buffering, pause→.part snapshots with offset resume, filename+size dedup with "skipped" notices, and true-delete removal with list refreshes.

DeepSeek Harness용 TypeSafe Jev(System One 의사결정 모델) 엔드투엔드 통합: jev_decision(choice/score/noul, 병렬, 타입 지정 답변 + 신뢰도), API 키와 스위치를 위한 Settings > Plugins 카드, 대화의 모든 결정(답변, 신뢰도, 지연 시간, 비용)을 보여주는 인라인 도구 뷰, 자동 가드(결정적 + Jev 위험/루프 점검), 로컬 /jev 대시보드, 온라인 검증 벤치마크(라벨링된 27개 질문에서 96.3%, jev-latest에서 중앙값 약 283ms, 2026-09-21). 설계상 정직함: 실제 API만 사용하며 mock 폴백이 없습니다.

Local SQLite long-term memory with CJK-aware search, BM25 and character n-gram cosine reranking, deduplication, and lifecycle management.

Animated DSH Web pet with model-adaptive output pacing, tool-state reactions, replaceable character packs, and a bundled skill for creating or extending pet animations.

Speaks your agent's reports in a voice you choose, compressing long reports before speaking. Character voices need no training - a 3-10 second reference clip clones one, and community-trained models work too - and no 6.4 GB GPT-SoVITS install: the plugin installs its own runtime and voice. An existing GPT-SoVITS can be used instead, and it is the same voice model.

Automatically highlights key points, definitions, warnings, and questions in completed assistant answers using one separate model call.

Pre-install compatibility gate for DSH plugins: locks the resolved version, checks peer/engines declarations, dry-installs into an isolated DSH_HOME and boots it with a timeout, blocks on any failure, and restores the profile if the real install fails. Optionally intercepts dsh-market install/update clicks. Compatible with DeepSeek Harness 0.2.x.

Plays a voice line or a sound when a turn finishes and a failure cue when a tool call or a turn errors — user aborts stay silent by default and subagent sessions are skipped, both configurable. Three alert modes: voice, effects, or off. Effects is the default and ships 20 built-in sounds (10 alert chimes + 10 nature), each assignable to the done, fail or approval event with per-row preview, so it needs no API key and no audio files; the voice route uses Volcengine TTS with cloned or preset voices, batch-imported voice IDs and fingerprint reuse that skips re-synthesis when text, voice and parameters are unchanged. A third approval slot exists but has no automatic trigger — it only plays when called manually. Playback goes through winmm/waveOut as-is, never changing the system volume or mute state, and the player is spawned detached so session teardown cannot silence it. Windows only.