Logseq Review Workflow Eval
logseq/logseq
Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…
Distill a research directory (produced by /research) into a single compact research.md containing a guideline-relative distillation of only the sources that were actually used in a piece of content.
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distill --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .claude/skills/research-distill && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .claude/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distillType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distill --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .agents/skills/research-distill && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .agents/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distill --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .cursor/skills/research-distill && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .cursor/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/iusztinpaul/ai-research-os-workshop.git --path plugins/ai-research-os/skills/research-distill--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distill --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .gemini/skills/research-distill && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .gemini/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distillInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .github/skills/research-distill && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .github/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install iusztinpaul/ai-research-os-workshop research-distill --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ai-research-os/skills/research-distill .opencode/skills/research-distill && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "research-distill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/research-distill into .opencode/skills/research-distill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "research-distill", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
research-distillDistill a research directory (produced by /research) into a single compact research.md containing a guideline-relative distillation of only the sources that were actually used in a piece of content.
Research Distill is an agent skill from iusztinpaul/ai-research-os-workshop. Distill a research directory (produced by /research) into a single compact research.md containing a guideline-relative distillation of only the sources that were actually used in a piece of content. Use this skill whenever the user wants to extract used references from research, create a research appendix for an article, distill research into what was actually cited, or produce a portable reference file from a research directory. Trigger when the user says things like "distill my research", "extract used…
Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Knowledge Management. The repository describes itself as: How to turn your Second Brain into a living research memory that your agents maintain. Workshop with slides, video and code. The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit dc66605. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Research Distill loads about 4.4k tokens when it runs. Until then it costs about 173 tokens; SKILL.md has 2,134 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from iusztinpaul/ai-research-os-workshop at commit dc66605, republished under its MIT licence (© iusztinpaul). 2,134 words, ~4,359 tokens.
.claude/skills/research-distill/SKILL.md (or your agent's skills folder).You take a research directory (produced by /research) and a set of content files the user is working on, and produce a single research.md containing only the sources that were actually used in that content, distilled to just the claims, quotes, and nuances the content actually leans on. Every source keeps its full metadata + URI envelope so the writer agent can drill back to the wiki source page or raw file when it needs more depth.
This is the audit/export side of the research system — it answers "which sources from my research actually made it into the final content, and what specifically from each one?" The output is a working appendix sized for a writer agent's context window, not a verbatim mirror of the research directory.
You need two things:
These are the files the user is actively working on — article guidelines, draft articles, notes, outlines, etc. The user may provide:
Projects/Content/My Article/guideline.md).md files in it)Read all content files and concatenate their text into a single content corpus for matching.
Locate the research directory using the same logic as /research:
research-*/ in the content's parent directoryworking-dir/ (the default research root, relative to where the skill is run) for research-*/Read index.yaml from the research directory.
For each source in index.yaml, determine whether it was actually used in the content. A source counts as "used" if either condition is met:
The content mentions the source by:
uri_highlights or uri_full filename)notebooklm origin sources)github_files (for github origin sources). Only one source entry exists per repo — the uri_full ARCHITECTURE.md — so matching a single referenced file path is enough to include that whole entry. The module docs it links to live in the same subfolder and are available via those links, not as separate sources.youtube origin sources). A timestamped mention like 12:30 near the video title or URL is enough to include the source.The content contains ideas, patterns, or concepts that are clearly traceable to the source. To check this:
uri_highlights if set (user-curated condensation — highest signal, smallest read)uri_full (the complete document — standard Obsidian notes, web seeds, and NotebookLM content land here because they have no Layer 2)null, fall back to the summary in index.yaml aloneBe conservative with traceable matches. Generic concepts like "agent loop" or "context window" appear in many sources — only match if the content uses a specific framing, example, or detail that's distinctive to that source. For example:
Create research.md in the same directory as the content files. The file contains one <details> block per matched source, ordered by relevance_score descending.
# Research Sources
> Distilled from `research-<slug>/index.yaml`
> Content: `<list of content file names>`
> Generated: YYYY-MM-DDTHH:MM:SS
> Sources used: N of M total
---
<details>
<summary>Source Title Here (score: 0.92)</summary>
<uri_highlights>path-to-highlights.md</uri_highlights>
<uri_full>path-to-full.md</uri_full>
<uri_source_page>wiki/sources/source-slug.md</uri_source_page>
<original_path>original vault or web path</original_path>
<origin>readwise</origin>
<relevance_score>0.92</relevance_score>
<tags>tag1, tag2, tag3</tags>
<summary>The index.yaml summary for this source.</summary>
<match_reason>Why this source was matched — explicit reference by title in section 3, and the "flush-before-discard" concept appears in the memory section.</match_reason>
### Relevant Claims
- Specific claim, datum, framing, or example from the source that the content draws on (one bullet per item, ≤30 words, concrete not generic).
- Another guideline-relevant claim — keep only what the content actually leans on.
### Verbatim Quotes
> "Distinctive phrase preserved exactly so the writer can cite it without re-reading raw."
> "Up to ~3 quotes per source; only when the guideline plausibly cites them or they anchor a distinctive framing."
### Nuances
- Caveat, counter-position, edge case, or qualification the guideline depends on. Omit this subsection if none apply.
### Wiki Pointers
- wiki/concepts/<slug>.md — one-phrase reason it's relevant
- wiki/entities/<slug>.md — one-phrase reason it's relevant
</details>
---
<details>
...next source...
</details><summary> tag (the HTML one, child of <details>): Source title + score in parentheses. This is what's visible when the block is collapsed. For github sources, suffix the title with — GitHub repo so the origin is legible at a glance (e.g., weave-cli — GitHub repo (score: 1.00)).
Metadata XML tags: One tag per field from index.yaml:
<uri_highlights>: Filename of the key-highlights file<uri_full>: Filename of the full document file, or null if none<uri_source_page>: The Layer 1.5 wiki source page (wiki/sources/<slug>.md) if present in index.yaml, else null. This is the writer agent's primary drill-down target when it needs more depth than the distilled body provides without jumping all the way to raw.<original_path>: The original vault path or URL<origin>: obsidian, readwise, web, notebooklm, github, pdf, or youtube<relevance_score>: The numeric relevance score from index.yaml (1.0 = seed; otherwise derived from the researcher's high/medium tag)<tags>: Comma-separated tag list<summary>: The source summary from index.yaml<readwise_location>: (Readwise sources only) library (user manually saved) or feed (ingested from an RSS subscription the user chose). Emit this tag only when origin is readwise and the field is present in index.yaml.<nlm_source_id>: (NotebookLM sources only) The NLM source UUID<nlm_notebook_title>: (NotebookLM sources only) The notebook's human-readable title<github_repo_url>, <github_commit_sha>, <github_branch>, <github_files>: (GitHub sources only) Emit these when origin is github. <github_files> is a comma-separated list of the referenced file paths — surfacing it here makes the distilled research.md self-contained for audit.<youtube_video_id>, <youtube_url>, <youtube_channel>, <duration_seconds>, <transcript_source>, <transcript_language>, <transcript_language_code>, <transcript_is_generated>, <timestamps_available>: (YouTube sources only) Emit these when present in index.yaml.<match_reason>: A 1-2 sentence explanation of why this source was included — what explicit reference or traceable idea linked it to the content. This helps the user (and future agents) understand the connection.
Distilled body: Replace the source's prose with a guideline-relative distillation. Each block has up to four subsections — emit any subsection only if it has content for this source; omit it entirely otherwise. Do not reproduce the raw layer verbatim; the URI tags above already point at it.
### Relevant Claims — One bullet per specific claim, datum, framing, or example from the source that the content draws on (or directly supports). Each bullet is one line, ≤30 words, concrete. Generic concepts the source shares with many others ("uses an agent loop", "RAG matters") never earn a bullet — only source-distinctive content does. If the source contributes nothing the content actually leans on beyond an explicit citation, this subsection can be a single bullet naming what was cited.### Verbatim Quotes — Up to ~3 short quotes preserved byte-for-byte (punctuation, casing, ellipses included), rendered as Markdown blockquotes. Only include quotes the guideline plausibly cites or that anchor a distinctive framing the writer agent might want to reproduce. This is the only lossless element of the body — it exists so the writer never round-trips to raw just to cite a phrase. When uncertain about exact wording, prefer pulling the phrase as a quote rather than paraphrasing it into a Relevant Claims bullet.### Nuances — Caveats, counter-positions, edge cases, scope limits, or qualifications from the source that the guideline depends on or could miss without. Skip the subsection if none apply.### Wiki Pointers — Paths (relative to the research dir) of wiki/concepts/<slug>.md, wiki/entities/<slug>.md, wiki/comparisons/<a>-vs-<b>.md, or wiki/questions/<file>.md pages that exist on disk and are relevant to this content via this source. One bullet per pointer, with a one-phrase reason. These are drill-down targets, not embedded content. Skip the subsection if no relevant wiki pages exist for this source.Read in this order and stop once you have enough signal to populate the subsections:
uri_source_page (wiki/sources/<slug>.md) if present — already condensed Layer 1.5 by the source_writer agent (extended summary, key claims, quotes, connections, entities, concepts). This is the primary distillation seed. Pull claims and quote candidates from here first.uri_highlights if present (Readwise-curated highlights — high signal per token, good for quote sourcing).uri_full as fallback, or to confirm exact wording for verbatim quotes.summary from index.yaml if every layer above is missing.Then re-read the content corpus and filter aggressively: keep only items the content actually draws on or plausibly cites. Everything else stays in the raw file, reachable via <uri_full> / <uri_source_page>.
Soft cap ~300 words per source body (sum across all subsections). Exceed it only when nuance genuinely demands. If the only available layer is summary, the body may be just one or two bullets — that's the expected shape, not an error. Note any missing layers in <match_reason>.
GitHub sources use the same four-subsection contract with two adjustments: rename ### Relevant Claims to ### Relevant Contracts, and rename ### Wiki Pointers to ### Module Pointers. Soft cap rises to ~500 words to accommodate the breadth across modules.
### Relevant Contracts — One bullet per guideline-relevant module. Each bullet names the module and summarises (≤30 words) the interface, behaviour, contract, or data structure the content draws on, including the names of key types/functions/files the guideline references. Only modules that pass the unchanged relevance check (github_files entry, module name mention, or specific traceable idea) earn a bullet.### Verbatim Quotes — Same rules. Quotes can come from ARCHITECTURE.md or any module spec.### Nuances — Design tradeoffs, invariants, init-time/order constraints, or scope limits the guideline relies on.### Module Pointers — repos/<repo>/ARCHITECTURE.md plus one bullet per guideline-relevant module spec file (repos/<repo>/<module>.md). Module-doc filename is the kebab-case of the leaf parent directory name (e.g., src/pkg/vectordb/interfaces.go → <repo>/vectordb.md, src/cmd/eval/run.go → <repo>/eval.md); if the computed filename doesn't exist, consult ARCHITECTURE's Module Index outline for the correct slug. List relevant modules in the order they appear in ARCHITECTURE's Module Index (alphabetical fallback). These are pointers only — never embed module specs verbatim. Surface the trigger (file path or distinctive idea) for each module in <match_reason> so the user can audit relevance.If uri_source_page, uri_highlights, and uri_full are all null or missing on disk (rare — fetch failure during /research): emit only the metadata block, set the body to a single line under ### Relevant Claims noting "Source body unavailable; see <match_reason> for citation context", and explain in <match_reason> which layers were missing.
Separator: Use --- between each <details> block for readability.
Tell the user:
<uri_full> / <uri_source_page>.research.mdresearch.md — rough count via chars / 4. Helps the user budget the writer agent's context.index.yaml (uri_source_page, uri_highlights, uri_full) doesn't exist on disk, fall back through the read-order list; if all are missing, see the "Missing layers" note in rule 4. It's normal for uri_highlights to be null for most non-Readwise sources and for uri_source_page to be null on very early research dirs — that's the expected shape, not an error.research.md if the content actually uses them. The question is "was it used?", not "was it important to the research?"© iusztinpaul, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in plugins/ai-research-os/skills/research-distill of iusztinpaul/ai-research-os-workshop.
Open the folder on GitHubat commit dc66605
Research Distill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Research Distill this skilliusztinpaul/ai-research-os-workshop | 179 | — | ~4.4k | Automated safety check: Pass | MIT | |
| Logseq Review Workflow Evallogseq/logseq | 45k | — | ~1k | Automated safety check: Pass | AGPL-3.0 | |
| Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill | 2.5k | 2 repos | ~3.2k | Automated safety check: Pass | None | |
| Obsidian CLIAtmosphere/atmosphere | 3.8k | 13 repos | ~795 | Automated safety check: Pass | Apache-2.0 | |
| Esm Cjs Risk Scanlogseq/logseq | 45k | — | ~3.3k | Automated safety check: Pass | AGPL-3.0 | |
| Knowledge Searchdataelement/bisheng | 12k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 |
logseq/logseq
Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…
sdyckjq-lab/llm-wiki-skill
Fetch any URL and convert to markdown using Chrome CDP. An agent skill from sdyckjq-lab/llm-wiki-skill.
Atmosphere/atmosphere
Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more.
logseq/logseq
Scan Logseq ClojureScript Node/Electron targets for npm module loading risks, especially ESM-only packages that may fail when loaded through js/require or shadow-cljs require-based shims.
dataelement/bisheng
Search the user's knowledge bases and knowledge spaces (企业知识库检索).
outline/outline
Save the current conversation, a decision, or a set of notes as a document in an Outline collection; use when the user wants to keep what was discussed in their knowledge base.
iusztinpaul/ai-research-os-workshop
Health-check a research directory produced by /research. An agent skill from iusztinpaul/ai-research-os-workshop.
iusztinpaul/ai-research-os-workshop
Expert guide for the NotebookLM CLI (nlm) and MCP server - interfaces for Google NotebookLM.
iusztinpaul/ai-research-os-workshop
Build, extend, AND query a persistent LLM-maintained wiki for any research topic.
iusztinpaul/ai-research-os-workshop
Generate a multi-form answer (Marp slide deck, matplotlib chart, Obsidian Canvas, or social content brief) from one or more wiki pages in a research directory and file the output back into…
iusztinpaul/ai-research-os-workshop
How to use the Readwise CLI — access highlights, documents, and your entire reading library from the command line
Categories
Distill a research directory (produced by /research) into a single compact research.md containing a guideline-relative distillation of only the sources that were actually used in a piece of content. Research Distill is an agent skill from iusztinpaul/ai-research-os-workshop.md containing a guideline-relative distillation of only the sources that were actually used in a piece of content.
Research Distill fits situations like: the user wants to extract used references from research; create a research appendix for an article; distill research into what was actually cited; produce a portable reference file from a research directory.
Run `npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a claude-code`. Or copy the skill folder (plugins/ai-research-os/skills/research-distill in iusztinpaul/ai-research-os-workshop) into .claude/skills/research-distill in your project. Claude Code loads it when a task matches its description.
Run `npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a codex`. Or copy the skill folder (plugins/ai-research-os/skills/research-distill in iusztinpaul/ai-research-os-workshop) into .agents/skills/research-distill in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add iusztinpaul/ai-research-os-workshop --skill research-distill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-distill, .gemini/skills/research-distill, .github/skills/research-distill and .opencode/skills/research-distill in your project.
SKILL.md names no scripts, command-line tools or credentials: Research Distill is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Research Distill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.4k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Research Distill: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
iusztinpaul (a GitHub user) maintains it in iusztinpaul/ai-research-os-workshop, which has 179 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 27, 2026.
Source: iusztinpaul/ai-research-os-workshop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.