How do I install the Deep Research Custom Data skill?

Run npx skillmds@latest add closedloop-technologies/deep-research-custom-data in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Deep Research Custom Data safe to use?

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Which AI agents work with Deep Research Custom Data?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Deep Research Custom Data free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Deep Research Custom Data?

closedloop-technologies (@closedloop-technologies) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Deep Research Custom Data by closedloop-technologies

Name: Deep Research Custom Data
Rating: 4.2 (5 reviews)
Author: closedloop-technologies

Deep Research Custom Data

Build and validate custom-corpus inputs for deep research from Google Docs, YouTube, local files, S3, arXiv, databases, Slack, email, tickets, and similar non-web sources. Use when preparing private or domain-specific source manifests, validating source safety, or turning custom data into cited research inputs.

File contents

Custom Data Deep Research

Use this skill when the research corpus is not just the open web. The goal is to turn private, semi-structured, or domain-specific sources into a cited corpus that a deep research agent can inspect safely.

Workflow

Define the research question and allowed sources.
Export or read sources with read-only credentials.
Convert each source into markdown or JSON plus metadata.
Build an evidence manifest with stable IDs and offsets.
Run the research agent over the allowed corpus.
Normalize the report, findings, uncertainties, and method into OKF.

Source Guidance

Source	How to Use	Citation Anchor
Google Docs / Drive	Search/read Drive files, export Docs, preserve file IDs and revision times.	file ID, title, URL, heading, revision timestamp
YouTube	Pull transcript and metadata; prefer official captions.	video URL, channel, upload date, timestamp range
Local files	Crawl only allowlisted paths, hash files, convert PDFs/docs/text.	relative path, sha256, modified time, page/line/heading
S3	Use read-only IAM, sync scoped prefixes, save object manifest.	`s3://bucket/key`, version ID, ETag, row/page/byte offset
arXiv	Fetch metadata and PDFs by arXiv ID and version.	arXiv ID/version, authors, section/page
Databases / CSV	Export query, schema, row counts, and sampled/aggregate evidence.	query ID, table, primary key, aggregate definition

Prompt Contract

Research question:
<question>

Allowed corpus:
<source IDs or OKF bundle paths>

Rules:
- Use only allowed sources unless public web supplementation is explicitly requested.
- Cite source ID plus exact anchor for every factual claim.
- Separate directly supported findings from inferences.
- Record missing, conflicting, stale, or low-confidence evidence.
- Return an OKF-compatible report.

Manifest Validation

Create a JSON manifest before indexing custom data. Validate it with:

python .agents/skills/deep-research-custom-data/scripts/validate_manifest.py \
  staging/custom-corpus/manifest.json

The validator checks corpus metadata, supported source types, stable source IDs, and citation anchors before a research agent uses the corpus.

Use docs/examples/custom-data-corpus.example.json as the minimal fixture for benchmark and CI validation.

OKF Output

Use concept files for source records and research outputs:

sources/<source-id>.md with type such as Google Drive Document, YouTube Transcript, Local File Evidence, S3 Object Evidence, or Scholarly Paper.
claims/claim-register.md with type Evidence Claim Register.
method.md with type Research Method.
report.md, findings.md, and uncertainties.md for the final research output.

See docs/custom-data-sources.md for detailed extraction and governance guidance.

Safety

Confirm authorization before accessing private data.
Use read-only credentials and scoped folders, prefixes, or queries.
Do not commit raw private extracts unless the repository is approved for that data.
Preserve retention/deletion requirements in method.md.

---name: deep-research-custom-datadescription: Build and validate custom-corpus inputs for deep research from Google Docs, YouTube, local files, S3, arXiv, databases, Slack, email, tickets, and similar non-web sources. Use when preparing private or domain-specific source manifests, validating source safety, or turning custom data into cited research inputs.---# Custom Data Deep ResearchUse this skill when the research corpus is not just the open web. The goal is toturn private, semi-structured, or domain-specific sources into a cited corpusthat a deep research agent can inspect safely.## Workflow1. Define the research question and allowed sources.2. Export or read sources with read-only credentials.3. Convert each source into markdown or JSON plus metadata.4. Build an evidence manifest with stable IDs and offsets.5. Run the research agent over the allowed corpus.6. Normalize the report, findings, uncertainties, and method into OKF.## Source Guidance| Source | How to Use | Citation Anchor || --- | --- | --- || Google Docs / Drive | Search/read Drive files, export Docs, preserve file IDs and revision times. | file ID, title, URL, heading, revision timestamp || YouTube | Pull transcript and metadata; prefer official captions. | video URL, channel, upload date, timestamp range || Local files | Crawl only allowlisted paths, hash files, convert PDFs/docs/text. | relative path, sha256, modified time, page/line/heading || S3 | Use read-only IAM, sync scoped prefixes, save object manifest. | `s3://bucket/key`, version ID, ETag, row/page/byte offset || arXiv | Fetch metadata and PDFs by arXiv ID and version. | arXiv ID/version, authors, section/page || Databases / CSV | Export query, schema, row counts, and sampled/aggregate evidence. | query ID, table, primary key, aggregate definition |## Prompt Contract```textResearch question:<question>Allowed corpus:<source IDs or OKF bundle paths>Rules:- Use only allowed sources unless public web supplementation is explicitly requested.- Cite source ID plus exact anchor for every factual claim.- Separate directly supported findings from inferences.- Record missing, conflicting, stale, or low-confidence evidence.- Return an OKF-compatible report.```## Manifest ValidationCreate a JSON manifest before indexing custom data. Validate it with:```bashpython .agents/skills/deep-research-custom-data/scripts/validate_manifest.py \ staging/custom-corpus/manifest.json```The validator checks corpus metadata, supported source types, stable source IDs,and citation anchors before a research agent uses the corpus.Use `docs/examples/custom-data-corpus.example.json` as the minimal fixture forbenchmark and CI validation.## OKF OutputUse concept files for source records and research outputs:- `sources/<source-id>.md` with type such as `Google Drive Document`, `YouTube Transcript`, `Local File Evidence`, `S3 Object Evidence`, or `Scholarly Paper`.- `claims/claim-register.md` with type `Evidence Claim Register`.- `method.md` with type `Research Method`.- `report.md`, `findings.md`, and `uncertainties.md` for the final research output.See `docs/custom-data-sources.md` for detailed extraction and governanceguidance.## Safety- Confirm authorization before accessing private data.- Use read-only credentials and scoped folders, prefixes, or queries.- Do not commit raw private extracts unless the repository is approved for that data.- Preserve retention/deletion requirements in `method.md`.

Frequently asked questions