@f1star/dsh-research

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@f1star/dsh-research is an installable DeepSeek Harness bundle for evidence-first paper work. It is a DSH bundle plugin, not a Codex plugin, and runs inside an existing DSH profile.

The bundle adds local native-text PDF reading, a durable paper library, and an auditable research-information workflow. It preserves exact source anchors and keeps quoted evidence distinct from authored notes, inferences, normalizations, comparison decisions, and syntheses.

What it adds

This standalone bundle mounts both the research services and their model-facing tool consumers in the selected profile. Installing it is an explicit grant to every agent started through that profile: each agent can see the research tool schemas and their stable prompt guidance.

Prerequisites

• A compatible dsh installation with pnpm available on PATH.
• A target profile containing @deepseek-ai/dsh-base followed by @deepseek-ai/dsh-web-app. The shipped web profile has this composition.
• Model credentials available through the normal DSH credential sources.
• Local PDFs readable under the session's filesystem permissions. Relative file paths resolve from the session workspace.

Install and run

Install the bundle into the shipped Web profile:

dsh plugin --profile web add github:F1star/dsh-research
dsh --profile web --dump-config
dsh web

The install command initializes the shipped web profile when it does not exist. The configuration dump lets you confirm that the @f1star/dsh-research layer and its research rows are present before booting the profile.

A GitHub install follows the selected Git ref. After a tag or commit you trust is available, pin it for reproducible installation:

dsh plugin --profile web add github:F1star/dsh-research#<tag-or-commit>

Restart a running profile after adding, updating, or removing the bundle.

Recommended workflow

1. Import a PDF with paper_import, inspect headings with paper_outline, find relevant blocks with paper_search, and call paper_read before relying on a passage.
2. Register the retained document with paper_library_register. Keep the returned paper, source-version, document, block, parser-version, physical-page, and quote-hash identifiers with your notes.
3. Create a research question, capture exact evidence, and record reading notes or passage questions. Write source statements separately from explicit inferences.
4. When comparing numeric results, normalize the method, dataset, metric, value, unit, split, uncertainty, evaluation protocol, and conditions for each paper. Record an explicit comparison protocol only after the retained fields are compatible.
5. Use the matrix and audit views to find missing or stale support, then write structured synthesis findings that cite active source claims.

You can describe the task in natural language; the agent chooses the tools. For example:

Import papers/one.pdf, show me its outline, and find passages about the evaluation dataset. Read the surrounding blocks before summarizing them. Register the paper, create a research question about dataset effects, capture exact evidence for each source statement, and show the audit view before synthesizing a conclusion.

Configuration

The bundle defaults live in cordis.patch.yml. A profile's own cordis.patch.yml is applied later and can override a row by id. A row override replaces its complete config value rather than merging individual keys, so retain parserProvider: pdfjs when overriding f1star-research-document unless another registered parser is intentional.

For a custom profile, compose the bundles in this order:

1. @deepseek-ai/dsh-base
2. @deepseek-ai/dsh-web-app
3. @f1star/dsh-research

Installing this bundle into a custom profile that contains only the base bundle fails because the filesystem, tool, system-prompt, and durable-storage services supplied by the Web layer are absent.

Data and limitations

• Paper identities, source observations, research questions, evidence, notes, claims, entities, observations, comparison protocols, and syntheses are stored in the selected profile's durable storage and can be visible across sessions using that storage.
• Parsed PDF pages are process-local. After a restart, import the PDF again before reading blocks or capturing new evidence. The library records identities and observations, not the source PDF bytes.
• Evidence records retain exact block text and provenance, but reconstructing an historical PDF still requires your own durable copy of the source file.
• PDF.js extracts native text only. Scanned or image-only documents require OCR outside this bundle and do not support a text claim from the import.
• Search and library matching are lexical. The bundle does not provide semantic retrieval, remote DOI or arXiv verification, automatic claim clustering, or automatic entity resolution.
• Numeric observations and comparison protocols are authored normalizations. The bundle does not silently convert units or aliases, rank results, calculate deltas, infer statistical significance, perform meta-analysis, generate a finished literature review, or export citations.

Update or remove

dsh plugin --profile web update @f1star/dsh-research
dsh plugin --profile web remove @f1star/dsh-research

Removing the bundle stops mounting its services and tools. It does not delete source PDFs, and research records can remain in the profile's storage; manage that storage separately if you need archival or deletion.

See the DeepSeek Harness guide to packaging and installing bundle plugins for profile and layer behavior.

License

MIT. This standalone distribution is derived from the DeepSeek Harness research packages and retains their 2026 DeepSeek copyright notice. See LICENSE and THIRD_PARTY_NOTICES.md.