jinguanghai/deepseek-harness-forge-plugins

evidence-first

证据铁律守卫：模型声称成功但上下文中没有对应工具执行记录时，自动注入警告。

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dsh-evidence-first

Evidence-first guard plugin for DeepSeek Harness. 证据铁律：声称完成必须有实际执行证据。

Why

LLMs are statistical machines: they can hallucinate, misattribute, and — most dangerously — claim completion without actually executing anything. Tool architectures can structurally prevent phantom tools and phantom calls (schema validation, closed registries), but no architecture can prevent the fourth kind of hallucination: a model that says "done" with no evidence behind it.

This plugin closes that gap at the session layer:

Records every tool execution (tool/result events) per turn.
Scans assistant messages for completion claims (完成/成功/修复/搞定/通过…).
A claim with no nearby tool execution → the next pre-step injects a visible warning, forcing the model to either supply evidence or retract.
evidence_audit tool: full audit report for the human gatekeeper.

Install

# via cordis.patch.yml / bundle
- id: evidence-first
  src: link:./plugins/evidence-first

Or copy lib/index.js into your plugin directory and register it.

Config

Field	Type	Default	Meaning
`claimPatterns`	`string[]`	Chinese completion phrases	Regex sources for completion claims
`evidenceWindowTurns`	`number`	`1`	How many turns back counts as "nearby evidence"
`injectWarnings`	`boolean`	`true`	Inject visible warnings on the next pre-step
`registerAuditTool`	`boolean`	`true`	Register the `evidence_audit` tool
`maxEvidenceEntries`	`number`	`200`	Per-session evidence cap (memory guard)

Config is fail-loud: invalid values throw at load time, never silently fall back.

How it works

session/event — observe turn/start, tool/result, assistant/message
agent/pre-step — inject the pending warning into the next model request
ctx.tools.register — evidence_audit audit tool
Injected messages carry source: { kind: 'plugin', plugin: 'evidence-first', form: 'notice', summary: '证据铁律警告' } — the {kind:'plugin'} tag is load-bearing: untagged context would render as a user prompt.

Design notes

Prefers false positives over false negatives. A warning is cheap; a silent unverified "done" is expensive. Claims are flag-for-human, not auto-rejected.
Zero runtime dependencies. The plugin imports nothing from @deepseek-ai/* at runtime — it compiles standalone and loads in any dsh environment.
Evidence window defaults to 1 turn (same turn or the previous turn). Adjust via evidenceWindowTurns for longer tool chains.

License

MIT

✅ Official convention compliance (官方规范合规)

DeepSeek Harness CONTRIBUTING.zh.md states that the project cannot accept external PRs, and directs the community to create and share plugins (tag repos with dsh-plugin). This plugin follows that path — it is a drop-in Cordis plugin, built to the same conventions as first-party plugins:

Official convention	This plugin
Named exports `{ name, Config, inject, apply }`	✅ same pattern (`apply` is default; `inject` injects the evidence contract into the model prompt)
`Config` with fail-loud validation (issues array + `throw` in `apply`)	✅ invalid patterns → `issues`; semantic errors → loud failure
Assembly via `cordis.patch.yml` bundle	✅ one-layer insert patch
Zero modification of official source	✅ pure event hooks (`turn/start`, `assistant/message`, `tool/result`, `pre-step`) + one registered tool (`evidence_audit`)
`package.json` per first-party standard	✅ `exports`/`types`/`files`/`peerDependencies` (`@deepseek-ai/cordis`, `dsh-agent`, `dsh-tools`)
i18n README (`README.i18n.yaml`)	✅ en/zh
Zero npm runtime deps	✅ except `@deepseek-ai/schemastery` (Config validation, same as first-party)

Verification (evidence first, of course): 5 unit tests (warning injection on evidence-less claims, no false-positive with real tool execution, tool registration, fail-loud Config, source-tag integrity) — 5/5 pass; plus an end-to-end headless run where the model actually executed a shell tool and claimed completion → no false warning, no inject errors.

Share: repo jinguanghai/deepseek-harness-forge-plugins (tagged dsh-plugin).