deepseek-continuity

场记 / Continuity

A DeepSeek Harness plugin that gives an agent local image / speech / music / SFX generation and remembers what it made — the same character stays the same character across every call, and a failed generation is never allowed to pass as a success.

Runs locally. Models are lazy-loaded per request and released when idle, so when you are not using it the GPU is untouched — 0.21 GiB resident, measured. You can play a game on the same card.

场记 is the continuity supervisor on a film set. Their entire job is two things: make sure the costume, hair and props match between takes, and catch the mistake on set before it is cut into the film. That is exactly this plugin's job.

Install

uvx --from dsh-continuity continuity-setup

That one command does the whole backend: preflight → build the engines → fetch only the weights this machine can use → start them.

The PyPI distribution is dsh-continuity (the import name stays continuity_mcp). It is not continuity-mcp — that name on PyPI belongs to an unrelated project, so do not uvx continuity-mcp.

To run from source instead: uvx --from git+https://github.com/linxuhao/Deepseek-Continuity continuity-setup

Then add the plugin to your dsh profile. dsh plugin shells out to pnpm, so install that first if you have not (corepack enable pnpm); without it the command stops at pnpm not found on PATH:

dsh plugin --profile <your-profile> add dsh-plugin-continuity

Add it to a profile that already has an app bundle. If you point it at a new profile, dsh creates one containing only @deepseek-ai/dsh-base plus this plugin — no app, so booting it does nothing and hangs. Add the app yourself in ~/.dsh/profiles/<name>/package.json:

"dsh": { "profile": { "bundles": [
  "@deepseek-ai/dsh-base", "@deepseek-ai/dsh-headless", "dsh-plugin-continuity"
] } }

The bundle reads its settings from the environment, so export what continuity-setup printed for your machine before booting the profile:

export CONTINUITY_STATE_DIR=~/.continuity
export CONTINUITY_SD_SERVER=http://127.0.0.1:9020
export CONTINUITY_AUDIO_SERVER=http://127.0.0.1:9021

To wire it by hand instead — continuity-setup prints this block filled in for your machine:

- insert:
    - id: continuity
      name: '@deepseek-ai/dsh-mcp-client'
      config:
        serverName: continuity
        transport: stdio
        command: uvx
        args: ['--from', 'dsh-continuity', 'continuity-mcp']
        env:
          CONTINUITY_STATE_DIR: !!js process.env.CONTINUITY_STATE_DIR ?? ''
          SD_SERVER: !!js process.env.CONTINUITY_SD_SERVER ?? ''
          AUDIO_SERVER: !!js process.env.CONTINUITY_AUDIO_SERVER ?? ''

(the complete row, with every passthrough documented: bundle/cordis.patch.yml)

continuity-setup checks the machine before it downloads anything, and sizes the install to what it finds. Run continuity-setup --check first to see what it would do — that reads hardware and changes nothing:

体检结果:
  GPU     AMD Radeon RX 7800 XT (RADV NAVI32)  (16.0 GiB, 此刻可用 15.8 GiB, DISCRETE_GPU, vulkan device 1)
          未选 AMD Radeon RX 7900 XTX (RADV NAVI31) (24.0 GiB, 此刻可用 1.4 GiB)
          跳过 llvmpipe —— 软件渲染, 不是真显卡
  内存    30.9 GiB
  磁盘    3118.4 GiB 可用 / 需要 30 GiB
  生图    启用
  音频    启用
  抠图默认档  best

Two details in there that exist because the naive version is wrong:

• It skips llvmpipe. The software rasterizer advertises 30.9 GiB of "VRAM" (it is your system RAM) and would win any "pick the biggest card" contest. Everything would then run on the CPU — working, looking completely normal, and unusably slow.
• It picks by free VRAM, gates by total VRAM. On the machine above the 24 GiB card has 1.4 GiB actually free because another process holds it; picking by size would select it and then OOM. But "is this card good enough" is a hardware question, so that one uses the total — otherwise a 16 GiB card would be rejected for having a game open.

Minimum requirements

	Minimum	Notes
GPU	8 GiB VRAM	Peak is 6.80 GiB (measured). Requests are serialized, so peak is one model, not the sum.
GPU API	Vulkan 1.2+	No CUDA, no ROCm. Kernels are SPIR-V compiled at runtime.
Disk	30 GiB during install, 19.5 GiB after	17.4 weights + 2.1 runtime image + 8.5 build layers (reclaimable).
Host RAM	16 GiB (8 GiB workable — see below)	Driven by transient peaks, not idle.
CPU	any x86-64	Background removal runs on CPU.

Audio-only installs (see below) need 20 GiB during install, 9.5 GiB after.

All VRAM/RAM figures on this page are GiB (2³⁰ bytes), which is what rocm-smi and vulkaninfo report. An earlier version of this README labelled them GB; that was wrong and made the headroom look tighter than it is.

Vulkan instead of CUDA is not a preference — it is why this runs at all. ROCm miscomputes VAE decode on this GPU class (ROCm#6633): five decodes of identical input returned five mutually uncorrelated results. Vulkan/RADV compiles SPIR-V at runtime instead of looking up a per-arch kernel table, and is correct and faster here. The side effect is portability across all three vendors.

GPU vendors

	How the container gets the GPU	Status
AMD	/dev/dri + mesa RADV inside the image	Tested (RX 7800 XT, RX 7900 XTX)
Intel	/dev/dri + mesa ANV inside the image — same mechanism	Untested
NVIDIA	nvidia-container-toolkit injects the host driver (docker-compose.nvidia.yml)	Untested

I only have AMD cards, so I will not claim more than that. Nothing in the code is AMD-specific — no CUDA, no ROCm, no HIP, no /dev/kfd, no gfx targets — and ggml's Vulkan backend is widely run on NVIDIA. But "widely run" is not "I verified it".

The NVIDIA path is a genuinely different wiring, not just a different card: NVIDIA's Vulkan ICD lives in the host driver and must be injected by nvidia-container-toolkit, with NVIDIA_DRIVER_CAPABILITIES including graphics — the default compute,utility gives you working CUDA and an empty device list in Vulkan. continuity-setup detects NVIDIA, uses the right compose overlay, and tells you the path is unverified. Reports either way are welcome.

Host RAM in detail

Idle is negligible; the peaks are what sizes the machine.

operation	peak RSS
idle	0.52 GiB
music	0.50 GiB
speech	1.63 GiB
image (1024²)	4.94 GiB
remove_bg quality="best"	7.74 GiB
remove_bg quality="fast"	1.33 GiB

Background removal is the ceiling, and its cost is independent of input size — 256 / 512 / 1024 px all peak at ~6.8 GiB, because BiRefNet runs at a fixed internal resolution.

On 16 GiB everything works. Below 12 GiB, continuity-setup sets the default to quality="fast" (u2netp): peak drops to 1.33 GiB and it runs in 0.6 s instead of 7.2 s. On a typical game sprite the two are hard to tell apart by eye — checked side by side over a magenta backdrop with the edges zoomed. best remains the default where there is room, because the models do differ in principle on fine edges (hair, semi-transparent fringes), but treat fast as a legitimate choice rather than a degraded fallback.

One rule, not a tier list

Jobs are serialized, so at any moment exactly one model is needed. Everything else is released before the job starts. That is the whole VRAM policy. (The one exception is a split deployment: if the image backend is on a different host from the audio one, they are not competing for a card, so nothing is released — freeing local VRAM for a remote job buys nothing and costs a reload.)

It buys a property worth more than a few saved seconds: peak VRAM is a constant 6.80 GiB regardless of what you call, in what order. Measured over an alternating speech→image→speech→image sequence:

	peak	speech	image	6 calls
keep models resident	10.94 GiB	2.8 s avg	11.5 s	42.9 s
release what isn't needed	6.79 GiB	4.8 s	11.6 s	49.2 s

Keeping them resident is 16% faster and does not fit an 8 GiB card — and "voice a line, then draw something" is the most ordinary sequence there is. An earlier version of this README quoted 7.84 GiB for that overlap; that came from a lighter sequence I happened to test, and using it as the ceiling was wrong. A cloned voice keeps its reference audio resident too, which is where the rest comes from.

What the reload actually costs: 4.8 s instead of 1.2 s, and only on the first call after switching away. Ten dialogue lines in a row pay it once:

第 1 句 4.63s   之后九句平均 1.19s   十句合计 15.4s

So there is no VRAM tier list, and no 12 GiB threshold. Above 8 GiB every card behaves identically. Below 8 GiB the installer explains why image generation will not fit and asks whether to install the audio half alone — it does not quietly substitute a different product:

  生图    显存不足
          Fake GTX 1060 只有 6.0 GiB, 而生图实测峰值 6.80 GiB, 需要 8 GiB。
          换更小的生图模型省不下这部分 (Q4 与 Q8 峰值相同 6.60 / 6.59), 降分辨率也不行
          —— 瓶颈是那个 8 GiB 不量化的文本编码器。
          音频那半仍然可以装: 铸声/配音/音乐/音效/抠图都能用, 4 GiB 就够。

  ⚠️ 这张卡装不了生图那半。
     只装音频那半 (铸声/配音/音乐/音效/抠图)? [y/N]

The audio-only install is a real product, not a consolation prize: casting voices, dialogue, music, SFX and cutout all work in 4 GiB.

What does not adapt at all: the image model. Quantizing it does not move VRAM — Q4_0 (2.29 GiB of weights) peaks at 6.60 GiB, Q8_0 (4.01 GiB) at 6.59 GiB, identical. Lowering resolution does not help either (512 / 768 / 1024 all peak the same; only time changes). The bottleneck is the 8 GiB unquantized 4B text encoder. So there is no "medium" image tier to offer, only installed or not. (Q4_0 ships anyway — same VRAM, 1.7 GiB less disk.)

Going below 8 GiB for images means changing the text encoder or the model family. That is possible, but it moves identity pinning from native ref_images to IP-Adapter, which is not verified here — and identity pinning is the whole point.

The one thing that does still key off a resource is host RAM, and it is a different resource: below 12 GiB RAM the cutout default drops to quality="fast" (see above).

Zero residency

Measured on an RX 7800 XT with nothing else on the card:

	GPU
idle	0.21 GiB
during image generation	6.80 GiB
2 s after it finishes	0.21 GiB
during TTS	2.39 GiB
120 s after TTS	0.21 GiB

Images are free: the engine streams weights per request and never keeps them resident. Audio is released by an idle timer (AUDIO_IDLE_UNLOAD_S, default 120 s) — not immediately, because someone voicing ten lines in a row should not pay a reload each time. Reload costs nothing measurable: the same TTS request took 3.0 s both cold and warm, because weights are mmap'd and sit in page cache.

Requests are serialized and everything unneeded is released first, so peak = the single largest model, always. The idle timer covers the one case the rule cannot: after the last job there is no next job to trigger a release, so the timer does it. Closing the agent releases the VRAM too — the MCP server unloads on exit rather than leaving the engines holding it.

Two things it actually does

1. Identity survives across calls. Generation backends are stateless: ask for the same character twice and you get two people who merely resemble each other. Measured on Qwen3-TTS as pitch spread across four lines of one character — same voice description, same lines, the only variable being whether a reference was pinned:

voice under test	straight to the model	through Continuity
a bright narrator	125 Hz	5 Hz
an elderly gravelly voice	74 Hz	29 Hz

Two different voices, two different magnitudes, same direction. Read the ratio, not the headline number — how far a description drifts depends on the description. And treat f0 spread as a proxy, not the verdict: autocorrelation pitch tracking makes octave errors on low gravelly voices (an earlier run of the table above reported 76 Hz where the octave-corrected figure is 29), so the numbers above anchor each line's search range to the reference. The real acceptance test is listening to the audition clip, which is why create_actor hands you one.

What the number cannot show is the part that matters most: the drift is not random.

	pitch spread across 4 lines
default sampling	125 Hz
greedy decoding	242 Hz — worse
pinned reference	5 Hz

Under greedy decoding the seed is provably inert — seeds 5 / 99 / 777 produced one identical sha256 — so randomness was fully eliminated, and it still drifted 242 Hz. Identity is a function of the input text, not of the random draw. temperature=0 and top_k=1 cannot fix it. Only pinning to a reference artifact can.

create_actor(name, voice)          -> audition clip; listen before you commit
actor_tts(actor, text)             -> same timbre every line

create_character / create_animal / create_object (name, appearance)
subject_image(subject, scene)      -> same look, new scene / angle / outfit

Identity and wardrobe are separate: pin the face and build, then change clothes in the scene prompt. A reference in an indigo robe, asked for wearing heavy red armor, comes back in armor with the same face.

Already cast your character somewhere else? import_actor and import_subject pin an artifact you supply — a real voice recording, an ElevenLabs clip, a character sheet from another tool — and everything downstream behaves identically. Audio is normalized to 24 kHz mono for you (44.1 kHz stereo in, verified: reference f0 identical, and an imported actor tracks a natively-cast one to 11 Hz).

Looking at what it made

The pinning tools tell the agent to look at the reference before committing to it. So they return the image, not just its path — a 512 px JPEG (~35 KB) alongside the text, as MCP image content. There is no VLM in this plugin and there will not be one: a vision model wants its own VRAM, which would destroy the property that peak = the single largest model, and the 8 GiB floor rests on that. The harness already has a model; hand it the picture instead of running a second one.

Verified end to end on dsh 0.1.1-rc.1 with the vision model — plain deepseek-v4-flash does not accept images and answers INVALID_REQUEST: This model does not support image:

- id: agent-default-model
  config:
    provider: deepseek-official
    model: deepseek-v4-flash-vision-exp

Asked to pin "a square metal lantern with EXACTLY FIVE blue glass panels and a green handle" and then check the render against that description item by item, the agent answered:

面板数量 — 不符合。 图中实际可见的是 4 块蓝色面板(正面 2 + 右侧面 2)，并非 5 块。 而且从"每面 2 块"的网格规律看，若其余两面同规格，总数应为 8 块。

It counted, it disagreed with the prompt it had just been given, and it said what it actually saw. That is the loop the statistical checks cannot close: they catch a grey PNG, this catches "that is not the thing I asked for."

On a model without image input the block degrades to [image unavailable] and the run continues normally — observed on dsh, not assumed; the agent then says it received no image rather than guessing from appearance. CONTINUITY_INLINE_IMAGES=0 sends text only.

One older caveat, corrected: an earlier experiment here had a self-hosted 27B VLM score 9/9/10 on chest renders whose lids were visibly the wrong shape, and I had written that off as "VLM judges are blind to geometry". The panel-counting result above is evidence that was a statement about that model, not a general law. Writing geometry explicitly into appearance is still the cheaper fix, but the check is now worth running.

2. Degenerate output is refused. A backend that miscomputes returns a perfectly well-formed all-zero WAV, or a flat grey PNG, with HTTP 200. Every artifact is checked (image standard deviation, audio RMS, non-finite samples) and the call fails loudly rather than reporting success over garbage. Cutouts additionally get a quality report — mostly transparent, nothing removed, subject shattered into fragments, holes eaten through the subject — each with a specific warning instead of a silent pass.

Plus remove_bg: diffusion models draw "transparent background" as an opaque checkerboard; this turns it into a real RGBA cutout, which sprites require. And gen_sfx, which synthesizes sfxr-style game SFX procedurally — bit-identical for a given seed, milliseconds, no GPU — because a diffusion model is the wrong instrument for a 40 ms coin pickup.

Tools

19 tools. Everything returns absolute local file paths, not URLs — the agent and the engines are on the same machine, so a path can go straight into your game project without a download step, and there is no file server to run or misconfigure.

voice	create_actor import_actor actor_tts list_actors delete_actor generate_speech
look	create_character create_animal create_object import_subject subject_image list_subjects delete_subject generate_image
audio	generate_music gen_sfx
post	remove_bg slice_sheet
meta	continuity_status

generate_image and generate_speech exist for one-offs and say so in their own descriptions: they explicitly tell the agent that what they produce will not come back on the next call, and point at the pinning tools for anything recurring.

Two audiences per result

Every tool returns two descriptions of the same call:

	who reads it	what it is
content	the LLM	the Chinese prose, ⚠️ warnings and all — unchanged, it is the prompt
structuredContent	your program	a typed object; the model is in results.py, its JSON Schema is the tool's outputSchema

// generate_image
{"ok": true, "error": null, "warnings": [],
 "path": "/home/you/.continuity/generated/img_1787322514_9a3f.png",
 "width": 1024, "height": 1024, "seed": null, "clamped": false}

// remove_bg, on a bad cutout — the ⚠️ is in both halves, never only in the prose
{"ok": true, "warnings": ["抠图结果很可能不对: 被去掉的区域细节密度是主体的 68% …"],
 "path": "…/cut_1787322526_5381.png", "mode_used": "rembg", "model": "u2netp",
 "transparent_ratio": 0.551}

// any failure — the prose stays the instructive Chinese message that tells the LLM what to do next
{"ok": false, "error": "actor '郭靖' 不存在 —— 先调 create_actor(…) 铸声, 再用它说台词。", "warnings": []}

Do not regex the prose for paths. That prose is a prompt: it gets reworded whenever the agent's behaviour needs it to be, and a regex that stops matching fails silently — you get an empty path, not an error. ok and path are the contract; the Chinese is not.

The shape is uniform. ok is always present and is the only field worth branching on first: when it is false, only ok / error / warnings are meaningful and everything else is null. Every ⚠️ in the prose has a matching string in warnings. Paths are always absolute.

Casting and pinning (create_character / create_animal / create_object / import_subject) return the reference image inline and structured content — those four are annotated Annotated[CallToolResult, …], which is the one form mcp 2.0 accepts for "several content blocks plus a declared output schema". The models are validated on every call, so a field that drifts away from what the prose says raises instead of shipping.

Limits, and why each one exists

Every number here is a measured failure boundary, not a policy.

limit	value	what happens past it
line length	200 chars	600 chars wedged the GPU: amdgpu GPU reset(6), device lost, an unrelated process on the other card killed. 200 is half the largest known-safe value.
reference audio	15 s	~0.19 GiB VRAM per second: 15 s → 6.59 GiB, 30 s → 9.04 GiB. 15 s is the last value that stays under the image peak, so voice never becomes the ceiling. One value for every card — 3–10 s is already enough to pin a timbre, so a bigger cap on bigger cards would only mean "this clip imports on my machine and not on yours".
casting script	45 chars	It produces the reference audio, which is then re-read on every later line. Char count is a bad proxy (60 chars measured 19.1 s, not the 13.7 s the ratio predicts), so the real duration is checked after casting and reported.
image size	1024 px	1280 pushed VRAM to 14.5/16.4 GiB; 2048 sent the driver into restore_userptr_worker thrashing with the process stuck in uninterruptible D state — worse than a clean OOM.
music length	120 s	Not a safety limit: the engine silently truncates at 120 s and reports success. The limit turns that into an explicit clamped field.

Imported audio below 24 kHz is accepted but flagged: upsampling cannot restore the octave that was thrown away, so the clone comes out duller than the file you gave it. That is worth a warning rather than a silent pass — it is the same failure shape as everything else this plugin exists to catch.

Oversized inputs are handled differently by type, on purpose. An image that is too large is resized and the result is reported back to you (原图 2400x1600 → 存为 1024x682) — a scaled picture still depicts the same thing. Reference audio that is too long is rejected, not trimmed: cutting the tail off the audio would leave the transcript describing something the audio no longer says, and that alignment is exactly what the cloning depends on. Trimming it silently would hand you an actor that imported successfully and sounds like someone else.

How dsh runs it

Not lazily on first tool call — at profile boot. dsh-mcp-client's apply() awaits the connection and the tool listing before the fiber activates, so the tools exist the moment the agent starts. Two consequences worth knowing:

• failOnStartupError defaults to false. If the MCP server cannot start, the boot succeeds with zero tools registered and nothing draws attention to it — the agent simply reports that the tools do not exist. (Ask me how I know.) Set it to true in the row if you would rather the profile refuse to boot.
• Reconnect is on by default: 500 ms, doubling to a 30 s cap, 10 attempts, then it gives up and unregisters the tools. Each attempt spawns a fresh server process.

Shutdown is a three-step ladder owned by the MCP SDK, and it is why the VRAM claim above holds:

step	budget	what we do
close our stdin	2 s	server loop ends, normal exit, atexit releases the models
SIGTERM	2 s	signal handler releases, then os._exit — Python would not run atexit here
SIGKILL	—	nothing runs; the idle timer in the engine still frees it later

Measured: 0.16 s on the stdin path, 0.11 s on SIGTERM, both releasing. The unload call is capped at 1.5 s precisely because the budget is 2 — a slow engine must not push us into the SIGTERM step, where the release would not happen at all. And it fires unconditionally rather than consulting this process's own bookkeeping: VRAM belongs to the engine, which outlives any one server generation, so a reconnected generation has an empty ledger and would otherwise skip the release entirely.

Running it without dsh (streamable-http)

Default transport is stdio and nothing about the dsh path changes — continuity-mcp with no arguments behaves exactly as before. For a caller that is not spawning the process itself (an HTTP shell, a second machine, several clients sharing one loaded model), run it as a long-lived streamable-http server:

continuity-mcp --http                                   # 127.0.0.1:9030/mcp
continuity-mcp --http --host 127.0.0.1 --port 9030 --path /mcp   # same, spelled out
CONTINUITY_TRANSPORT=streamable-http continuity-mcp     # env instead of flags

flag	env	default
--transport {stdio,sse,streamable-http} (--http is shorthand for the last)	CONTINUITY_TRANSPORT	stdio
--host	CONTINUITY_HTTP_HOST	127.0.0.1
--port	CONTINUITY_HTTP_PORT	9030
--path	CONTINUITY_HTTP_PATH	/mcp

Point an MCP client at http://127.0.0.1:9030/mcp.

It binds loopback by default and you should leave it there. There is no authentication of any kind, and the tools write files to this machine's disk and delete actors and subjects. Binding 0.0.0.0 hands that to anyone on the segment — put a reverse proxy in front if you need it reachable. Port 9030 stays clear of the two engines (9020 / 9021).

The VRAM guarantee is unchanged over HTTP: image generation and TTS still share one process-wide lock, so several clients connecting at once means they queue, not that two models sit on the card together. What HTTP does change is the shutdown ladder above — a long-lived server is not being reaped by dsh, so the models stay loaded until AUDIO_IDLE_UNLOAD_S (default 120 s idle) frees them, or until you stop the process.

Bring your own backend (optional)

There are two independent backend URLs, so you can move one capability off-box and keep the other local:

	env var	what it must be
image	SD_SERVER	a stable-diffusion.cpp sd-server (/sdcpp/v1/img_gen + poll, accepts ref_images)
audio	AUDIO_SERVER	an audio.cpp audiocpp_server (/v1/tasks/run, /v1/tasks/unload_models)

Tell the installer which half is yours and it skips that half entirely — no weights, no engine, no VRAM gate — while the tools stay registered:

continuity-setup --sd-server http://your-box:9020      # 生图你自己供; 本地只装音频
continuity-setup --audio-server http://your-box:9021   # 反过来

That matters more than it sounds: without it, BYO-ing the image half still downloaded 10.1 GiB of image weights and started a local sd-server nobody would ever call, then refused to enable image tools because the local card was too small. On a 5.3 GiB integrated GPU, --sd-server turns "生图 显存不足" into "生图(BYO)" and downloads nothing.

Note the deliberate split from --no-image: that one means "I don't want this capability" (tools unregistered); --sd-server means "I supply this capability" (tools work normally).

Either half also works when set purely at runtime via the two env vars, whether you installed from PyPI or wired the dsh plugin — the cordis row passes them straight through. continuity_status names whichever side is unreachable. gen_sfx needs no backend at all.

Be clear about what "your own backend" means here: the same engine, elsewhere. It is not a provider abstraction. The client speaks sd.cpp's and audio.cpp's specific HTTP shapes, so you cannot point SD_SERVER at an OpenAI-compatible endpoint, a ComfyUI instance, or a bare IP-Adapter server and expect it to work. What it is good for: running the engines on a beefier box, or sharing one backend between several agents. (An earlier version of this README implied any reference-image-capable backend would do. That was never true of the code.)

One constraint if you go remote: identity pinning needs the image backend to accept a reference image. sd.cpp's ref_images is what the code uses; without it there is no pinning, which is the whole point.

Reference audio used to be a second constraint — the engine was handed a filesystem path and opened the file itself, so a remote audio backend meant casting succeeded and every line after it failed. That was not an engine limitation, it was the wrong endpoint: /v1/audio/speech takes the reference inline as base64 (5 MiB cap; a 15 s reference is ~720 KB), exactly the way the image path had always passed ref_images. Both halves are symmetric now and nothing has to share a directory.

Prior art

A survey of the current MCP ecosystem — MiniMax-MCP, openrouter-mcp-multimodal, AtlasCloud, the dsh vision/draw plugins, and four game-asset servers — found voice cloning in several, visual subject pinning in none, and output verification in none.

Layout

package.json + cordis.patch.yml   the dsh bundle (npm) — one plugin row, at the repo root
                                  so `dsh plugin add github:...` works, not just the npm name
src/continuity_mcp/               the MCP server: pinning, guardrails, verification, cutout,
                                  VRAM lifecycle
src/continuity_mcp/deploy/        compose + engine Dockerfile + weight manifest
pyproject.toml                    the PyPI distribution (dsh-continuity)

License

MIT