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Clearai Dsh — DeepSeek Harness 插件(DSH Plugin)
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clearai-dsh

Clearai Dsh

ClearAI:原生于 DSH 的认识论循环。

插件会安装到这里;不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add github:Clearailhc/clearai-dsh#e362acc1832006d92c8e29838233510c42b55c23
README兼容性版本

兼容性与来源证明

Clearai Dsh 以 clearai-dsh 发布,当前版本为 0.2.3。Plugin Hub 会校验它的 manifest,并保存精确安装来源,便于复现安装结果。

DSH 兼容范围
*
运行环境
web
发布来源
github
Registry 更新时间
2026/9/23

版本

0.2.3stable
2026/9/23
0.2.2stable
2026/9/18
0.2.0stable
2026/9/17
查看其余 4 个版本收起版本
0.1.6stable
2026/9/16
0.1.5stable
2026/9/16
0.1.3stable
2026/9/15
0.1.2stable
2026/9/12
最新版
0.2.3
DSH
*
HMR
重启进程
Tree shaking
未声明可安全裁剪
解包体积
未提供
文件数
未提供
Surface
web
许可证
Apache-2.0
发布源
github
GitHub
★ 456
周下载
0
最近提交
2026/9/23
查看源码 ↗
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报告问题

README

ClearAI

English · 中文

Your research, grown into an ontology.

ClearAI is an ontology discovery and exploration platform, built on two core concepts:

  • Domain ontology (what you get) — your project's own vocabulary, the knowledge entries established through the loop, and their graphs. At the end of a research session you hold a continuously growing knowledge structure, retrievable next round by concept.
  • Epistemic loop (how you get it) — a disciplined seven-stage path: frame, hypothesize, plan, observe, verify, evaluate, record. Every edge is tested by evidence and independent evaluation.

Other knowledge graphs pile up edges by extraction and assertion; here every edge has to be earned through the loop.

# Install (npm package, prebuilt — no build step, no allowBuilds prompt)
dsh plugin --profile web add clearai-dsh

Restart dsh web, then pick ClearAI in the preset picker at the top of a new session. That is the whole setup. Full install notes ↓

The epistemic loop (left) growing a domain ontology (right)

Left: the Epistemic Loop — seven stages. Its emerald fact dot is also the first node of the domain ontology on the right. Right: the ontology graph — dark is a concept, light is a value form, emerald an instance; the instance carries two contradictory assertions — the two readings are tinted amber, marking that they do not agree. The system reports the conflict; retracting or keeping is a human decision.


What you get: a domain ontology

A domain ontology that grows as you research:

  • Vocabulary — the language your project speaks: concepts, predicates, value forms, units. Conventions themselves carry no truth value; sentences written with them do.
  • Established entries — knowledge that passed the loop: each with its boundary, support level, and evidence chain. Each entry states its boundary explicitly, so it can be cited safely.
  • Ontology graph and entity graph — what your domain looks like (structure), and what you have actually verified (the state of play).
  • Conflict readings — contradictory conclusions surface automatically; the system reports them, and retracting or keeping is your decision.

How you get it: the Epistemic Loop

Most agent loops track one thing: whether the task is done. The Epistemic Loop also tracks what makes a conclusion trustworthy:

Task loopEpistemic loop
Driving questionWhat next?What do we know, and on what grounds?
CompletionThe model declares itThe system computes it from delivered evidence
VerdictWhoever did it, says soSeparated — above a level, the doer cannot judge themselves
FailureDeleted, retried, forgottenKept: a refuted hypothesis is a result, not noise
What accumulatesA chat transcriptAn ontology: every edge earned through the loop
The Epistemic Loop

Inside the ring is the instrument's read-out: the L0–L4 axis, the pre-registered threshold as a dashed line, and five observations with error bars — the supported one filled, the inconclusive drawn as a dashed circle, the refuted left in place with a slash through it (nothing is deleted). The emerald dot at the opening is the one reading that crossed the threshold and settled as a fact.

At runtime, the seven stages compress into four beats — plan, execute, observe, reflect. State is derived from the session record with no second store; the tools the model holds contain no field in which it could declare a step complete.

ClearAI does not claim recursive self-improvement. It provides the epistemic substrate a self-improving system would need. See Positioning and the OpenRSI survey.


Install and use

Recommended — install from npm:

dsh plugin --profile web add clearai-dsh

This installs the prebuilt package from the npm registry. Nothing is compiled on your machine, so there is no allowBuilds grant to approve — the plugin is ready the moment the command returns.

Also available — one-command installer:

npx clearai-dsh install

Same install underneath; it resolves the DSH CLI from your PATH (or through npx), installs into the web profile, and reads the composed config back so you are not taking "success" on faith. Use this if you prefer a guided path, or --lang zh|en to force the installer's output language.

Install from source (for development, not the normal path):

dsh plugin --profile web add github:Clearailhc/clearai-dsh

Git fetches source rather than build artifacts, so pnpm ≥10 will refuse to run the prepare script until you add an allowBuilds entry to the profile's pnpm-workspace.yaml. That grant means permission for this package's code to execute on your machine at install time — grant it only if you have read the source, and pin a commit. If you just want to use ClearAI, use the npm install above.

The installer's output follows your system language (--lang zh|en overrides it, doctor / seed / unseed take the same flag). Its only runtime dependency is zod; the graph stack is bundled into the client half at build time.

Restart dsh web afterwards (npx @deepseek-ai/dsh web), then create a session and switch to the ClearAI mode in the picker at the top:

  1. Open dsh web and click "New session";
  2. Click the current mode name at the top (default: Standard mode) to open the preset list;
  3. Pick ClearAI — its card reads "利用认识论循环构建可信本体。Build a trustworthy ontology through the epistemic loop.";
  4. Just ask your question. Ordinary Q&A runs as usual; once you set a goal and register hypotheses, the system enters knowledge mode by itself: known facts come to you, gaps stay visible, and conclusions earn their place.
The knowledge graph in ClearAI mode

The ontology graph in ClearAI mode — this real session grew 21 concepts and 9 predicates; the same ledger always yields the same picture. (UI shown is Chinese.)

If pnpm is not on PATH: npm install -g pnpm (do not corepack enable — it installs a version forwarder that may download a pnpm it cannot launch).

From the repository:

npm test                       # 15 suites
node tools/build-package.mjs   # assemble dist/ from source
node tools/verify-package.mjs  # rebuild on the spot, byte-compare
node docs/diagrams/build-hero.mjs   # redraw the product hero (needs google-chrome)

dist/ is generated and never committed. See DSH integration.


What it looks like

The middle column has two switchable views: Deliverables and Ontology. The right sidebar: Worldlines and External Brain.

Ontology — this view is your knowledge home. At the top, a graph band: the ontology graph (what your domain looks like) and the entity graph (what you have actually verified) toggle with one click; clicking a node or edge opens the knowledge inspector (definition / relations / assertions / evidence chain / history), and "filter by this" is an explicit action inside the detail view. Below that, the ontology shelf: established entries, each with assertion chips (click to see what the term means), boundary, and support level; contradictions surface automatically. The vocabulary maintenance block sits collapsed at the bottom — it auto-expands when a language exists before any sentence does.

The graph band: ontology graph and entity graph

The band — the ontology graph and the entity graph share one deterministic projection, so the same ledger always yields the same picture (captured from a real session: 21 concepts, 9 predicates).

Knowledge inspector: definition, relations, assertions, evidence chain

Open any node or edge: definition, relations, assertions, evidence chain, registration and revision history, all in one place. (UI shown is Chinese.)

Worldlines — when two routes genuinely disagree, they run as separate branches with their own readings; the losing one stays on record, and adoption is a human press.

Deliverables — the middle column keeps "what the plan declared" and "what actually exists on disk" apart.

External Brain — skills and memory as DSH-native entries in one merged catalogue.


Cases

  • Physical-world process experiment — sensor thermal drift: the full chain from raising terms to a conflict surfacing
  • AI for Science — convergence order of WENO reconstructions, and what "we could not resolve it" honestly means
  • Mathematics — keeping finite numerical evidence strictly separate from proof

Documentation

  • Positioning · Domain ontology design
  • Epistemic loop · Verification ontology · Loop philosophy
  • Design principles · Soul map · Glossary
  • Knowledge-native loop ledger (this round: triage / preflight / knowledge gate / graph / inspector; zh-CN)
  • Development plan (with real-run evidence from the Hengtong project)
  • Known gaps · Authority map · Release verification

Where it sits in DSH

ClearAI adds the epistemic layer on DSH's composition plane — one host package, one agent preset, one client module, with zero changes to the DSH engine. Working style is unrestricted, but nothing outside the governed path can write to the authoritative ledger (pinned by tests).

Work attribution

This project's work attribution unit is Jidian Qiyuan.

Star History

Star History Chart

License

Apache-2.0, see LICENSE.

Status

A local-first ontology discovery and exploration platform delivered as a DSH plugin. What is not yet implemented, and what has not been verified in a real browser, is written in Known gaps.