How do I install the Deep Research Okf Normalize skill?

Run npx skillmds@latest add closedloop-technologies/deep-research-okf-normalize 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 Okf Normalize safe to use?

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Which AI agents work with Deep Research Okf Normalize?

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 Okf Normalize free to use?

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Who published Deep Research Okf Normalize?

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

Deep Research Okf Normalize by closedloop-technologies

Name: Deep Research Okf Normalize
Rating: 4.666666666666667 (3 reviews)
Author: closedloop-technologies

Deep Research Okf Normalize

Normalize raw deep research outputs into a compact Open Knowledge Format bundle with source, finding, uncertainty, method, index, and log concepts. Use after provider runs when reports need portable structured markdown and citation-preserving validation.

File contents

OKF Normalize Research

Use this skill after a deep research agent returns a report. It turns a raw report into a portable markdown bundle aligned with Google's Open Knowledge Format draft: markdown files with YAML frontmatter, a required type field, optional index.md and log.md, and citations preserved as links where possible.

Command

python .agents/skills/deep-research-okf-normalize/scripts/normalize_to_okf.py \
  --input benchmark/results/perplexity-sonar/meta/q1_output.txt \
  --bundle-dir benchmark/results/okf/perplexity-sonar-meta

For ad-hoc text:

python .agents/skills/deep-research-okf-normalize/scripts/normalize_to_okf.py \
  --title "Deepresearch the deepresearchers" \
  --text "Paste or pipe the report text here" \
  --bundle-dir outputs/okf/deepresearchers

Bundle Shape

The generated bundle contains:

index.md - progressive-disclosure entrypoint.
report.md - the normalized source report concept.
findings.md - extracted claim bullets and citation-bearing lines.
uncertainties.md - caveats, conflicts, missing evidence, and stale-data risks.
method.md - provider, prompt, timestamp, and normalization notes.
log.md - update history.

Validation

python .agents/skills/deep-research-okf-normalize/scripts/validate_okf.py \
  benchmark/results/okf/perplexity-sonar-meta

Normalization Rules

Preserve raw claims before improving prose.
Keep citations as markdown links or source URLs.
Mark assumptions separately from measurements or cited facts.
Prefer concise concept files over one large report.
Do not reject unknown frontmatter fields when consuming an OKF bundle.

---name: deep-research-okf-normalizedescription: Normalize raw deep research outputs into a compact Open Knowledge Format bundle with source, finding, uncertainty, method, index, and log concepts. Use after provider runs when reports need portable structured markdown and citation-preserving validation.---# OKF Normalize ResearchUse this skill after a deep research agent returns a report. It turns a rawreport into a portable markdown bundle aligned with Google's Open KnowledgeFormat draft: markdown files with YAML frontmatter, a required `type` field,optional `index.md` and `log.md`, and citations preserved as links wherepossible.## Command```bashpython .agents/skills/deep-research-okf-normalize/scripts/normalize_to_okf.py \ --input benchmark/results/perplexity-sonar/meta/q1_output.txt \ --bundle-dir benchmark/results/okf/perplexity-sonar-meta```For ad-hoc text:```bashpython .agents/skills/deep-research-okf-normalize/scripts/normalize_to_okf.py \ --title "Deepresearch the deepresearchers" \ --text "Paste or pipe the report text here" \ --bundle-dir outputs/okf/deepresearchers```## Bundle ShapeThe generated bundle contains:- `index.md` - progressive-disclosure entrypoint.- `report.md` - the normalized source report concept.- `findings.md` - extracted claim bullets and citation-bearing lines.- `uncertainties.md` - caveats, conflicts, missing evidence, and stale-data risks.- `method.md` - provider, prompt, timestamp, and normalization notes.- `log.md` - update history.## Validation```bashpython .agents/skills/deep-research-okf-normalize/scripts/validate_okf.py \ benchmark/results/okf/perplexity-sonar-meta```## Normalization Rules- Preserve raw claims before improving prose.- Keep citations as markdown links or source URLs.- Mark assumptions separately from measurements or cited facts.- Prefer concise concept files over one large report.- Do not reject unknown frontmatter fields when consuming an OKF bundle.

Frequently asked questions