Skills

Agent-managed procedural memory: a skill_manage tool that creates, patches, disables and deletes the agent's own skills in user or project scope, with delete guards (agent-created marker, pin, path confinement) and a trigger-discipline prompt.

Import, install and invoke Agent Skills in DeepSeek Harness from Codex, Claude Code, OpenCode, GitHub or npm — composer-side ⚡ picker inserts /skill-name into the draft, and a Skill 管理 settings page offers per-source/per-skill import, expandable source paths with skill counts, uninstall and one-click restore from .trash.

Project documentation scaffolding skill: question-driven setup of project standards, AI-collaboration workflows, and memory discipline across DSH, Reasonix, Claude Code, and Codex.

Self-learning loop for dsh: record lessons, draft and promote skills, periodic curator review that corrects, consolidates, and retires skills.

Smart form skill: structured record-keeping for bills, ledgers and logs with auto-categorization and periodic reports.

Life-story skill: capture moments and fragments, build a user profile and generate novels, scripts or autobiographies.

Product research skill: analyze market, technology and supply chain, then output three-tier product spec proposals.

GEO brand audit skill bundle: AI-search visibility, recommendations, citations, competitor presence and content-gap diagnosis across 17+ AI engines (ChatGPT/Perplexity/Claude/豆包/DeepSeek/Kimi/文心一言).

Bundled Xiaohongshu/RED viral-note agent skill: hot-note research, note generation/rewrite, verification, authorized account analysis, QR login and controlled publishing.

Run Stata through DeepSeek Harness: the native Stata AI Skill service auto-starts with DSH (macOS/Windows binaries bundled), so you can run regressions, do-files and econometrics workflows with no manual setup.

Chain-of-thought leakage linter: zero-dependency CLI that finds AI-session residue in docs and comments (dead design citations, PR vantage, change narration, review choreography), shipping the cot-trim fixing skill as the installable bundle.

Rigorous open mathematics research suite: four agent skills (rigorous-open-math-research, manage-math-research-program, math-research-workflow, lean-verify) for theorem solving with adversarial audit, research program management, pipeline orchestration, and Lean 4 formalization audit; CI-verified tests and mechanical upstream sync.

Spec-driven development discipline for dsh: five keel skills (anchor/spec/probe/build/audit), three review tools and six spec templates enforce specs-before-code, assumption verification and scope control.

Bridge Pi Agent's skills and configuration into DeepSeek Harness

Bridge OpenAI Codex's memory, skills, and configuration into DeepSeek Harness

Provides a skill for the LinShang LS125 UV irradiance meter and its UVALED-X3 probe: the reverse-engineered 9600 8N1 link (28-byte frames, CRC-16/MODBUS, poll and clear commands), a capture and decode toolchain, and a verified STM32F103 host firmware that reads the probe without the original meter. All code and documentation written by an AI agent (DeepSeek Harness).

DSH plugin that registers the ai-eight-honors-eight-shames skill: eight checkable working rules for coding agents (verify before calling, confirm ambiguity, cite evidence, reuse before creating, test before claiming done, follow local conventions, separate fact from inference, keep changes reversible) plus a ninth rule that logs each user request and the change made in report files at the workspace root.

Build PowerPoint decks from a brief: plan and storyboard gates, per-page authoring through pptwise and ppt-master, a design-profile pass, page-image snapshots with a render audit, and a tool that hands those pages to an image-capable model for review.

On-demand skill tools for DeepSeek Harness: registers skill_search / skill_load / skill_ref so the model searches a staged skill library first and loads a skill body only when it needs it, keeping the library out of the resident session catalog and out of every turn's context; adds a Skills tab to the conversation view ring that reads back the whole session and lists which skills were actually loaded, and in which turn. Zero dependencies, and the records render client-side only, never entering the model's context.

DSH plugin that registers the ai-eight-honors-eight-shames skill: eight checkable working rules for coding agents (verify before calling, confirm ambiguity, cite evidence, reuse before creating, test before claiming done, follow local conventions, separate fact from inference, keep changes reversible) plus a ninth rule that logs each user request and the change made in two fixed report files at the workspace root.

Task-to-skill pairing meta-skill with a laziness ladder for minimal loading, a user-editable routing table, and a quarantine, SkillSpector scan, human-approval install flow.

Adds file-backed Chinese web-fiction planning, chapter writing, consistency review, rollback, and style-profile skills.

Ponytail lazy-senior-dev ruleset for DeepSeek Harness: a 7-rung minimal-code ladder (YAGNI, reuse, stdlib, native, installed deps, one line, minimum), six skills (main rules, over-engineering review, repo audit, debt ledger, gain scoreboard, help), a /ponytail session command for lite/full/ultra/off levels, and optional always-on ruleset injection via config. Vendored verbatim from upstream ponytail (MIT), zero tools registered, clean uninstall.

Skill that fact-checks a claim against the open web rather than model memory: it splits the claim into independently checkable fact points, routes each point to the primary-source ladder for its domain (policy, academic, medical, financial, disaster and accidents, food safety, science, education, sports, entertainment, judicial, and more), searches domestic and international ecosystems in parallel, fetches primary pages, grades sources by type, re-searches with debunking keywords, and reports conclusion-first with a full URL and citation position behind every statement, keeping unverified and undecidable points separate from confirmed ones. Three factual judgements can be routed to a TypeSafe Jev (System One) decision model when a key is configured, falling back to the skill's deterministic rules when it is not; a model verdict can only lower a confidence tier.