🍷 dsh-palate — an eye that grows

中文说明 · DeepSeek Harness plugin

Design-audit tools measure with a fixed ruler. dsh-palate trains an eye that grows.

Most design-review plugins ship a static ruleset and apply it forever — use them once or a thousand times, the judgment is identical. dsh-palate is the opposite: it keeps a taste corpus that accumulates. Every example you feed it and every principle you distill sharpens the judgment your agent draws on. The more you use it, the better its eye gets.

Why this exists

Taste is not a gift — it's pattern recognition built from exposure. See enough good and bad design, and the rules emerge. dsh-palate turns that into a mechanism an agent can actually use:

1. Feed — record designs you judged good or bad, and why
2. Distill — recurring lessons become codified principles
3. Review — critique a new design against the accumulated taste, not a generic checklist
4. Calibrate — record which recommendations actually helped; only confirmed helpful principles gain evidence, so the palate compounds honestly

What the agent gets

Ships with a starter palate of 12 foundational principles plus four transparent teaching examples (good and bad dashboards, a readable table, and generic landing-page boilerplate), so the first review has concrete evidence — then it grows from there.

The four starter examples are inserted only when the local taste database is empty. Installing or upgrading the plugin never overwrites an existing palate.

How it works

palate_add (good/bad + why)  ──▶  taste corpus (SQLite + Markdown mirror)
palate_learn (new rule)       ──▶  codified principles
palate_review (a design)      ──▶  review_id + principles + relevant examples  ──▶  agent writes grounded critique
        ▲                                                                            │
        └── palate_feedback (accept/reject + why) ──▶ effectiveness + accepted-principle evidence ─┘

• Storage: node:sqlite (built into Node ≥ 22) at $DSH_HOME/palate/, plus human-readable taste.md / principles.md mirrors. Zero runtime dependencies.
• Retrieval: a review ranks examples against the current description using local words, tags, and Chinese word fragments; when no precedent is relevant, it leaves the evidence empty instead of padding with recent entries.
• Feedback loop: every palate_review snapshots its evidence; palate_feedback records the outcome, while feedback.md and the panel show actual acceptance/rejection data.
• The panel: a draggable overlay shows examples studied, principles distilled, recent review subjects, the exact example refs cited by each review, and recent judgments.
• Vision pairing: feed it screenshots by reading them with a vision tool first (e.g. modlens_read_image), then pass the description to palate_review.

Honest framing

This is accumulated retrieval + codified principles + explicit feedback, not model fine-tuning. The plugin supplies learned taste as context; the model renders the critique. Only a user/agent-confirmed palate_feedback adds evidence to a principle, keeping judgment auditable through taste.md, principles.md, and feedback.md without retraining anything.

Install — copy, paste, confirm

# GitHub is the supported release channel.
dsh plugin --profile web add github:guo6x/dsh-palate

Restart a running dsh web process, then refresh the page. Installation is complete when a 👁️ button appears at the bottom of the sidebar. Click it to see the starter palate, its principles, and its feedback history.

Requirements: the DeepSeek Harness web profile and Node ≥ 22. The plugin uses only local SQLite storage — no account, API key, or embedding service is required.

Developing from a checkout instead? Run dsh plugin --profile web add . from the repository directory. The repository commits the lib/ entrypoints, so GitHub and path installs can start immediately without running an install-time build script.

See the learning loop in 90 seconds

Start a new chat and paste this safe, local-first task:

Build our first taste record for a dense analytics dashboard. Use palate_add to save one bad example: “all 12 KPI cards have equal visual weight, so the decision signal is buried”; tag it dashboard, hierarchy. Then use palate_review to critique “an analytics dashboard with twelve equal KPI cards, one primary revenue metric, and a small trend chart.” Explain which learned principles you used.

The response should name the matched record and starter principles instead of applying a generic checklist. Open the 👁️ panel to see the example count grow and the new review appear. If you adopt a recommendation, ask the agent to record palate_feedback for that review; only confirmed helpful principles gain evidence.

If the 👁️ button is missing

• Confirm the plugin is installed in the web profile: dsh plugin --profile web list dsh-palate.
• Restart the dsh web process after installing; a browser refresh alone cannot load new host code.
• Check that Node is version 22 or newer. The plugin has no additional runtime dependency to install.

Develop

pnpm install
node build.mjs        # esbuild → lib/index.js (host ESM) + lib/client.js (ModuleLoader bundle)
node tests/smoke.mjs  # pure-logic checks (no browser needed)

MIT licensed. Ideas and examples welcome — open an issue.

Known limitations

• No embedding-based semantic matching in the plugin itself — it retrieves locally by tags, words, and Chinese word fragments; the model does the deeper reasoning from the assembled context.
• Feedback is explicit — the plugin does not guess whether a user adopted a recommendation; call palate_feedback after a review to form effectiveness data.
• Markdown mirrors are read-only exports for v0.1 (human edit-and-merge-back is planned).
• Vision is delegated — pair with a vision tool to review screenshots.