dsh-moments-aieo

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An AIEO (AI Engine Optimization — the GEO/AEO practice of getting a brand cited by ChatGPT, DeepSeek, Doubao, Kimi, Perplexity and friends) delivery method, packaged as one DeepSeek Harness bundle. Installing it gives an agent the whole four-stage service flow — diagnosis → positioning → content → monitoring — as a named skill provider.

Plugin

Requires ctx.skills (inject: ['skills']).

The plugin body is deliberately thin: it mounts @deepseek-ai/dsh-skill-filesystem with includeDefaultRoots: false over its own skills/ directory, so this set registers under one provider name and never collides with same-named skills in ~/.dsh/skills or ~/.agents/skills. No scanner, watcher, or frontmatter parser is reimplemented here.

Config

Field	Default	Meaning
skillsDir	the package's own skills/	Directory holding the <name>/SKILL.md bundles. Point it at a working tree during development.
providerName	moments-aieo	Provider name registered on ctx.skills, keeping this set separable from the user's own roots.

Install

dsh plugin --profile web add github:Kenerlee/dsh-moments-aieo   # straight from GitHub
dsh plugin --profile web add file:/path/to/clone                # from a local clone

Then add the package to the profile's bundle list in ~/.dsh/profiles/web/package.json:

{ "dsh": { "profile": { "bundles": [
  "@deepseek-ai/dsh-base",
  "@deepseek-ai/dsh-web-app",
  "dsh-moments-aieo"
] } } }

The bundle's own cordis.patch.yml inserts the row, so no profile patch is required. Override it by id in ~/.dsh/profiles/web/cordis.patch.yml when you want your own skill directory:

- id: moments-aieo
  config:
    skillsDir: /absolute/path/to/your/skills

Verify without booting:

dsh --profile web --dump-config | grep -A 4 'id: moments-aieo'

Skills

Skill	Purpose
aieo-diagnosis	Brand AI-visibility diagnosis; emits a report plus the first draft of the question bank
aieo-positioning	Positioning analysis on an AIEO-adapted April Dunford method; iterates the question bank
aieo-query-miner	Real search-term mining from whitelisted platform exports only; refuses to invent terms
aieo-monitoring	Periodic visibility, share-of-voice, content-quality and conversion tracking
moments-aieo-dashboard	Renders monitoring reports into an interactive HTML dashboard
content-creator	Brand-voice-consistent SEO content production
humanizer-zh	Strips AI writing tells from Chinese text
landing-page-cloner	High-fidelity landing-page replication

The four AIEO skills share one artifact chain: the question bank the diagnosis drafts is what positioning corrects, content consumes, and monitoring measures against. Running them out of order is allowed and produces a weaker bank.

Model Experience

Indirectly, through @deepseek-ai/dsh-tool-skill: this provider's names and capped descriptions appear in the model's skill catalog, and skill(name) loads the selected SKILL.md body plus its resource base. Paths, provider ranks, and the mount configuration stay hidden from the model.

KV Cache effect

Catalog only. Registration adds eight rows to the catalog digest once; skill bodies enter history only when the model loads one.

Known Limitations and Deferred Work

• Tool names are written in Claude dialect — the skill bodies name Read, Write, and mcp__playwright__browser_*. Under dsh those are bash, str_replace_editor, the fs tools, and whatever dsh-mcp-client mounts. The frontmatter allowed-tools key is ignored by dsh's parser: it neither errors nor restricts anything.
• Web mode disables the host-level provider — dsh-web-app sets skill-filesystem: disabled because agent presets own local discovery. This bundle registers globally and preset agents read the merged catalog, so the set stays visible; a deployment that isolates its presets from global registrations would not see it.
• No build step — the plugin ships as plain .mjs with no TypeScript source, no lib/, and no type declarations. It is twenty lines; a consumer wanting types writes them.
• Reference cases are not distributed — the diagnosis skill's worked client examples live outside this repository.
• Chinese-first content — every AIEO skill body is written in Chinese, and the scoring rubrics assume Chinese-language AI search platforms.

Who built this

The method comes from real AIEO delivery work — brand diagnosis, positioning, question-bank construction and monitoring for consumer, healthcare, SaaS and franchise clients. The tooling is open source; the industry baselines and the judgement of what to do with a low score are not things a Markdown file can carry. moments.top

Ran a diagnosis? Open a Discussion with your score and industry (no brand name needed). Real numbers across industries are what turn a scoring rubric into a benchmark, and the aggregate goes back into this repo.

License

MIT