插件全景
Know what exists before you build what is missing.
これは DeepSeek Harness(DSH)プラグインです。当サイトは GitHub README、インストール情報、メンテナンス状況、公開セキュリティシグナルをまとめています。
上流で中国語 README が提供されていないため、リポジトリのオリジナルコンテンツを表示しています。
DSH Landscape
简体中文 · Roadmap · Contributing
Know what exists before you build what is missing.
DSH Landscape answers one practical question: given a capability need, what already exists in the DeepSeek Harness ecosystem, what is still missing, and should you use, extend, investigate, or build?
It combines plugin discovery, evidence-based maturity, coverage-aware verdicts, and fresh verification. A repository name or README claim alone never counts as proof that a capability is solved.
What you get
- A native DSH tool:
dsh_landscape. - Need-first
analyze,find, andbriefCLI workflows. - A portable Agent Skill for Codex, DSH, and other compatible hosts.
- Structured evidence with provenance, maturity, freshness, uncertainty, and next-action guidance.
Quick start: install as a DSH plugin
Install the verified, reproducible revision into your DSH profile:
dsh plugin --profile web add github:cyanseek/dsh-landscape#1ef0e1ebbb5e84e8d2feed31ae71d1b97322f6f9
dsh --profile web --dump-config
The bundle registers dsh_landscape. Ask DSH a need such as:
Check whether DSH already has a native Linear integration. Show the evidence and tell me whether to use, extend, or build.
No separate Landscape model key is required inside DSH. The tool retrieves and verifies evidence; the host model makes the final semantic decision.
Runtime options
| Argument | Default | Meaning |
|---|---|---|
need | required | Capability or integration described in natural language |
limit | 10 | Maximum matching projects, from 1 to 20 |
fresh | true | Live-check negative or uncertain results through GitHub search |
There is no required plugin configuration. Anonymous GitHub search works out of the box; an existing GITHUB_TOKEN can increase API limits. Its value is never included in tool output.
Uninstall
dsh plugin --profile web remove dsh-landscape
DSH unregisters the tool when the bundle is removed. Replace web with the profile you actually use.
Use as an Agent Skill
Use once without permanent installation:
npx -y skills use cyanseek/dsh-landscape --skill dsh-landscape --agent codex
Or install the portable Skill:
npx -y skills add cyanseek/dsh-landscape --skill dsh-landscape -g -a codex -y
The generic prompt-only form works with other capable hosts by omitting --agent codex.
Run the CLI
The GitHub-source commands work without an npm release:
npx -y github:cyanseek/dsh-landscape find "browser automation"
npx -y github:cyanseek/dsh-landscape analyze "Linear integration for DSH"
npx -y github:cyanseek/dsh-landscape brief "Linear integration for DSH" --format agent
When used by a host Agent, Landscape reuses that host's semantic capability. For standalone semantic analysis, configure one OpenAI-compatible provider:
export DSH_LANDSCAPE_API_KEY="your-key"
export DSH_LANDSCAPE_BASE_URL="https://your-provider.example/v1"
export DSH_LANDSCAPE_MODEL="your-model"
npx -y github:cyanseek/dsh-landscape analyze "Linear integration for DSH"
Without a key, retrieval still works and negative results remain explicitly provisional. Inspect the active intelligence mode without making a model request:
npx -y github:cyanseek/dsh-landscape status --json
The package is not advertised as published to npm. Use the verified GitHub source commands above.
Result contract
A result includes:
- interpreted capabilities and ranked matching projects;
- maturity and source evidence for each match;
- covered and missing sub-capabilities;
- discovery completeness and freshness;
- a
covered,partial,crowded,placeholder-only,gap, orunknownverdict; - a
use,extend,build,avoid-duplication, orinvestigaterecommendation.
gap is deliberately hard to reach. Incomplete or stale discovery, failed fresh verification, or search-only reasoning downgrades a negative conclusion to unknown.
Surfaces
| Surface | Best for | Semantic reasoning |
|---|---|---|
| DSH plugin | Native checks during a Harness session | DSH host model |
| Agent Skill | One-off or installed Agent workflows | Host Agent |
| CLI | Automation, JSON output, and briefs | Configured provider or transparent search-only mode |
| Node API | Application integration | Chosen by the caller |
| Static API | Browser-safe snapshot exploration | Search-only |
Node API
import { findPlugins, loadAliases, loadSnapshot } from 'dsh-landscape'
const { snapshot } = await loadSnapshot()
const aliasData = await loadAliases()
const matches = findPlugins('browser automation', { snapshot, aliasData, limit: 5 })
Versioned static data is generated under site/api/v1.
Data and trust
The current snapshot merges and deduplicates two attributed public sources:
- the GitHub
dsh-plugintopic; - the Awesome DeepSeek Harness catalog.
Trust rules:
placeholder,prototype,installable, andtestedrequire observable evidence;verifiedis reserved for runtime acceptance evidence.- Archived repositories are excluded from active competition by default, and forks are marked and down-ranked.
- External repository code is never executed during discovery.
- Every verdict-affecting project record retains source provenance.
- API key values are never printed, serialized, or stored in fixtures.
Limitations
- DeepSeek Harness is in developer preview and may introduce breaking changes.
- Coverage is limited to configured public sources, not every GitHub or npm project.
- Metadata-based maturity is conservative and does not replace runtime verification.
- Live verification depends on GitHub availability and rate limits.
- The static website is a search-only surface; use DSH, an Agent Skill, or a configured provider for semantic judgment.
Development
Node.js 22 or newer is required. The repository has no runtime dependencies and the GitHub plugin install runs no lifecycle build scripts.
npm run release:check
npm pack --dry-run
The release check runs syntax checks, tests, the static build, data validation, and Skill validation. See CONTRIBUTING.md, SECURITY.md, and the issue forms before submitting factual changes.
License
MIT © 2026 cyanseek
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