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Immutable. This exact content is served forever at /api/v1/blob/0b1cec47ba64498c.

# clawock

> AI argues. Code settles. Even the losses stay on the page.

clawock is an agent-native, harness-agnostic investment decision-workflow
engine. An external agent runtime (Claude Code, Codex, OpenClaw, DeepSeek
Harness, or your own) owns the model, conversation, memory and tools; clawock
owns the decision contract: certified evidence, a mandatory opposing case,
deterministic money and FX reconciliation, and a public scorecard the model can
never grade itself.

A real Hong Kong + US account has run it for 90+ days: 177 judgments settled by
Python, live account return −15.95%, every loss on the page, every number
reproducible from `clawock audit-resettle`.

## Key pages

- [GitHub repository](https://github.com/KCNyu/clawock): source, issues, PRs
- [Live dashboard](https://kcnyu.github.io/clawock/): positions, risk, self-graded scorecard
- [Daily briefs](https://kcnyu.github.io/clawock/briefs.html): published morning reads (bilingual)
- [Evidence & refutation](https://kcnyu.github.io/clawock/evidence.html): what was tested and what failed
- [FAQ](https://kcnyu.github.io/clawock/faq.html): questions new users ask
- [简体中文 README](https://github.com/KCNyu/clawock/blob/master/README.zh.md)

## Install

```bash
python -m pip install clawock
clawock workflow install investment-decision --workspace ./my-decision
clawock init ./my-decision --workflow investment-decision
clawock run prepare --workspace ./my-decision
```

Or throw the repository URL at any agent and let it follow the three-step
skill flow (prepare → write `decision.json` → publish). A no-model end-to-end
proof runs with `bash examples/cli/minimal-run/run.sh`.

## For DeepSeek Harness users

An official-style skill package is available: `dsh plugin --profile web add clawock-dsh`
(published to npm as `clawock-dsh`). The same decision contract works from a
pure CLI, an OpenClaw skill, a Claude Code instruction, a Codex AGENTS.md, or a
DSH agent — see [examples](https://github.com/KCNyu/clawock/tree/master/examples).

## What makes it different

- The model can never grade itself: LLMs propose, Python settles.
- One thesis counts once; repeated restatements collapse into one episode.
- Factors need out-of-sample validation before they influence decisions.
- The ledger must reconcile before anything is published.
- Every number on the README reproduces from `clawock audit-resettle` — if it
  doesn't match, the project loses.

## Honesty policy

The live scorecard keeps every eligible result, losses included, including the
fact that active recommendations have not beaten buy-and-hold. This repository
is a personal record and portable workspace — not investment advice, not a
copy-trading service.