ARIS on DeepSeek Harness
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The dsh-aris distribution branch. The full ARIS project — every workflow, the docs, and the other host adaptations — lives on main.
❗ Updated codex-cli to 0.154 or later? Get dsh-aris 0.1.1: dsh plugin --profile web add dsh-aris@latest, then restart the profile. 0.154 removed codex mcp-server, which 0.1.0 spawned, so that profile no longer starts. 0.1.1 ships ARIS's own bridge instead; it works on 0.153 too.
Runs the ARIS research workflow inside DeepSeek Harness: all 82 skills in the native skill catalog, with cross-model adversarial review through Codex.
The skills are unmodified. This bundle is one configuration layer plus a short adapter — it patches no Harness code and forks nothing.
Install
dsh plugin --profile web add dsh-aris
Restart the profile afterwards; plugin code loads at startup, so reloading the page is not enough.
dsh plugin shells out to pnpm, which the Harness does not bundle. Without it on PATH the install exits before doing anything.
The command resolves the package from the npm registry. Without npm access, install the same tarball from the GitHub Release instead — pinned to this version, and dsh plugin update does not apply to it:
dsh plugin --profile web add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/releases/download/dsh-aris-v0.1.1/dsh-aris-0.1.1.tgz
Prerequisites
Codex CLI, installed and authenticated. It is the independent reviewer. The bundle reaches it through ARIS's own bridge, mcp-servers/codex-exec/server.py (shipped in the package), which runs each call as codex exec: codex-cli 0.154 removed codex mcp-server, verified on codex-cli 0.153.4 and 0.154.0. Nothing here overrides the model or reasoning effort. A call that names neither uses ~/.codex/config.toml; a skill that names them pins them for the whole thread. ~/.codex/config.toml is the reviewer posture contract. ARIS expects a non-DeepSeek family at xhigh:
model = "gpt-6-astra"
model_reasoning_effort = "xhigh"
If the bridge cannot start — no python3 — the Harness fails to boot rather than running a composition with no reviewer. That is deliberate: ARIS without an independent reviewer is not ARIS. A Codex that is missing or logged out surfaces later, on the first reviewer call, as an error result carrying Codex's own message.
Python 3.9 or newer on PATH as python3, for the bridge. macOS's system Python qualifies; no packages are installed.
A DeepSeek API key, through the Harness Models page or DEEPSEEK_API_KEY.
Behind an HTTP proxy, start the Harness with NODE_USE_ENV_PROXY=1. Node's fetch ignores http_proxy otherwise, and model requests fail with TRANSPORT. It must be set when Node starts; configuration cannot repair an already-started process.
Optional — bound the executor too. ARIS's reviewers already carry scope limits: the block that forbids proposing hashes, defensive scaffolding, corner-case hardening, and over-mechanized judgement is embedded in the skills that produce review prompts, and applies with no setup. Nothing bounds the executor the same way. Two ways to close that, and they are different things:
- ARIS's own limits, one flag. The
aris-scope-limits row ships disabled; set disabled: false in your profile's patch to apply the same block to the executor. It reads the packaged skills/shared-references/review-scope-limits.md at load, so the reviewer path and the executor path cannot drift apart. Off by default because it spends tokens on every request.
- The full HERO contract. Paste its canonical block into the project's
AGENTS.md or CLAUDE.md, or into $DSH_HOME/AGENTS.md for every dsh project — the Harness loads those files itself. This bundle does not vendor HERO's text; its canonical home is HERO's own RULES.md.
Verify
dsh --profile web --dump-config | grep -A2 aris-
Then, in a session, type / — the skill menu lists the ARIS skills. To check the part that matters, ask the model to call mcp__codex__codex with a trivial prompt, report the threadId it can see, then continue that thread once with mcp__codex__codex-reply. A visible threadId is what makes multi-round review work. The bridge keeps one record per thread under ~/.codex/state/codex-exec/threads/, so a thread continues after a Harness restart.
The ARIS tab
In the Web UI a session gains an ARIS tab beside Chat and Trajectory. It is
read-only: it shows what the reviewer and the loop's own artifacts say, and
never writes, advances a round, or turns a score into a completion decision.
It reads review-stage/REVIEW_STATE.json from the session's workspace, so it
stays empty until an auto-review-loop run finishes a round there.
The top section is the reason the tab exists: who reviewed this round, and
whether ARIS could verify that the reviewer belongs to a different model family
than the executor. On the Codex backend that verification is unverified by
design — Codex reports its own model, but nothing independently attests the
executor's, so ARIS records identity_assurance: caller_declared. Read it as
"route-consistent, not attested".
Two honest limits are shown in the tab and worth repeating: state is written
when a round finishes, not continuously, so a long round displays the previous
one; and completed means the loop ended — a positive assessment or the round
cap — never that the work was acquitted.
What the layer changes
| Row | Effect |
|---|
agent-default-model | executor becomes deepseek-v4-pro |
aris-skills | mounts the 82-skill corpus, publishes ARIS_REPO, restores Codex's threadId, serves the ARIS tab |
aris-codex | the codex-exec bridge as the codex MCP server, 20-minute call budget, pinned to a stable working directory; threads resume across Harness restarts |
The corpus mounts at the bundled rank, so a project or user skill of the same name wins. The executor default is a deployment default, not a lock: a saved model setting or a per-session choice overrides it.
Developing against a checkout
ARIS_REPO=/absolute/path/to/aris NODE_USE_ENV_PROXY=1 \
dsh --profile web --patch /absolute/path/to/aris/dsh/checkout.patch.yml
Skill edits take effect without a restart. ARIS_REPO is required; without it the load fails naming the variable.
This overlay is not equivalent to the installed bundle: it cannot restore Codex's threadId, so multi-round codex-reply — and therefore the hard-tier Debate Protocol — works only through the installed bundle. The two are mutually exclusive: applying the overlay to a profile that already has the bundle fails on a duplicate row id. Remove one.
Known limits
- Tracks one Harness release. DeepSeek Harness is a developer preview; this bundle is verified against
0.1.1-rc.1. Every surface it uses survived rc.5 → 0.1.1-rc.1 unchanged, but that is not a promise about the next one.
- No dsh packages are declared as npm dependencies. In-box packages are host-provided and resolve from the Harness installation through the profile module fallback. The Harness's own rule keeps
@deepseek-ai/dsh-* out of dependencies, and the packages version in lockstep with the CLI, so any range this bundle pinned would fight the version the user already has installed.
web_fetch is off. Stock dsh ships it disabled and this bundle does not enable it, which would mean depending on a provider package. Skills that reach the web use web_search, or bash with curl.- Codex's own reasoning is not in the Harness log. Only the verdict returns. The call arguments and the verdict are logged; the reviewer's intermediate work stays on the Codex side.
- A verdict over 50 KB is spilled to a file with a preview left in context. Raise
maxInlineBytes on the spill-policy row if your reviews run longer.
