Plugins

Local-first failure evidence capsules for DeepSeek Harness sessions

A headless-first macOS Computer Use driver for DeepSeek Harness with AX-first observations, window-bound multimodal evidence, host-owned approval, and evidence-backed action receipts

Deterministic session evidence reports for DeepSeek Harness: /transcript writes styled single-file HTML / Markdown / JSON reports from canonical session logs, /stats prints a terminal stats card, /archive writes per-session ZIPs

Make the AI remember how you think, not what you said: actively captures your principles, redlines, and working habits across sessions — with memory capacity, graceful decay, and human-readable reasoning for every entry. 0.7.0 brings an intuitive persona portrait with evidence tracing.

Merges other sessions' context into the current turn: evidence-weighted conflict resolution, retraction of claims whose premises were retracted, trust labelling that resists cross-session prompt injection, and budget-bounded packing. Ships a benchmark.

Audits a DSH profile for capabilities nothing consumes: inventories the declared composition rows, counts how often each registered tool and skill is actually called in the session logs, separates calls that were made from calls that returned a result, and reports the ones with no observed consumer. Ships a skill that fixes the evidence-chain format for completion claims.

DSH plugin for bounded agent evaluation: compare an original with candidate prompt or supported configuration changes against user-supplied checks, record evidence and costs, and leave adoption to the user.

Automatically orchestrates complex DeepSeek Harness requests. It detects when delegation is useful, builds a dependency-aware plan, assigns bounded researcher, architect, backend, frontend, tester, and documentation workers, collects structured evidence, and sends the results to a final reviewer. The bundle atomically enables the workflow engine and model-facing subagent tools; disabling the plugin disables its complete tool group as well.

Project PDF library with background indexing and cited page highlights for DeepSeek Harness Web

XingTu 26 production-grade AI agent skills, packaged as a DeepSeek Harness (DSH) skills plugin — token compression, code review, safe refactor, migration, evidence review & more. 26 个生产级 SKILL.md 技能，DSH 一键安装即用。

Native Goal Autopilot Harness for DeepSeek Harness (dsh): plan-execute-audit gates, mechanical enforcement, and evidence-bound closeout as an out-of-tree plugin bundle.

DeepSeek Harness plugin for CVE impact retesting: fingerprint a target, judge it against a CVE's affected range, gather passive evidence, and report a verdict with confidence and limitations.

Score a GitHub repo's contribution-friendliness from live merge and maintainer evidence.

Project-based learning coach: a subject-agnostic teaching engine plus swappable domain packs (Unity/C# included), shipped both as a DSH skill and as an installable bundle. The skill protocol enforces attempt-first teaching and graded hints; a zero-dependency validator rejects ability claims their evidence does not support and blocks stage acceptance while blockers are open.

Verifiable authorization, execution evidence, and offline delivery verification for DeepSeek Harness

Read-only MCP server over the DSH plugin-certification registry, exposing a plugin certification grade, its snapshot and the five-dimension evidence behind it.

A persistent DevRel workbench for official DeepSeek Harness Discussions: triage, topics and evidence-first reporting.

Test-insight plugin that turns repository facts and change risk into evidence-backed test plans and isolated, reviewable test drafts.

Merged, version-locked plugin upgrade skill for DeepSeek Harness (0.1.3-alpha.1 to 0.1.5-rc.1 as two closed legs): an evidence-bound version card plus a zero-dependency 20-seam scanner, shipped as a bundle skill and an npx CLI.

Records scoped workflow rules and correction reminders with bounded evidence, local settings, and optional Brain Hub integration.

Measures what an llm-pi-ai endpoint really accepts — context window from its own model list, output-token limit and reasoning levels read out of its own range errors, image support from a generated test image — then reports declared values that contradict that evidence and rewrites them only after confirmation.

Whole-book translation production line for DeepSeek Harness: deterministic chapter detection and segmentation, per-chapter subagent translation, glossary control, full-book review, targeted revision, and auditable meta.json evidence.

Evidence-first frontend release checks: real-browser content, accessibility, runtime, network, and baseline regression reports with screenshots.

Evidence-first attention supervision for DeepSeek Harness with C0–C3 policy, deduplicated Inbox alerts, quiet hours, and redacted outcome records.