Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Capture, summarize, and organize knowledge from URLs, YouTube videos, documents, and files. Proactively recall stored knowledge when relevant.
Capture, summarize, and organize knowledge from URLs, YouTube videos, documents, and files. Proactively recall stored knowledge when relevant.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Manage a personal knowledge base that extracts content from artifacts, generates summaries, and organizes entries into fluid categories. When to activate this skill: Explicit knowledge operations: the user says "save this", "add to knowledge", "capture this article", "summarize this video", "sort knowledge", "organize categories", "import knowledge", etc. Proactive recall: the user asks about ANY topic where the knowledge base might have relevant entries. This includes general questions like "what's a good workout routine", "how should I approach this design problem", "what do I know about sleep training", etc. When in doubt, check the knowledge base -- it is an extension of memory. Search it whenever the user's question touches a topic that could plausibly have been captured previously.
The knowledge base path is not hardcoded. On first invocation: Check agent memory for a previously stored knowledge base path. If no path is stored, ask the user. Suggest ~/Documents/knowledge/ or a sensible default. Save the chosen path to the agent's persistent memory so it persists across sessions. On subsequent invocations, read the stored path. Do not ask again unless the path no longer exists.
Date format: YYYY-MM-DD (ISO 8601 date only) Filename format: {descriptive-slug}--{YYYY-MM-DD}.md Slug: lowercase, hyphenated, 3-6 words describing the content Raw and summary files for the same artifact share the same filename Summary length: 100-400 words
{knowledge-base-path}/ README.md CHANGELOG.md unsorted/ raw/ summary/ {category}/ _category.md raw/ summary/ Category directories are created on demand when knowledge is sorted into them.
On first invocation: Resolve the knowledge base path (see Configuration above). If README.md does not exist at the knowledge base path, create: README.md (see README Template below) CHANGELOG.md with an initial entry unsorted/raw/ and unsorted/summary/ directories (use mkdir -p via Bash) Save the resolved path to agent memory if not already stored.
If git is available and the knowledge base directory is not already a git repository, initialize one (git init). The knowledge base benefits from version control โ it provides history for category reorganizations, a safety net for bulk imports, and makes it easy to sync across machines. After meaningful operations (adding knowledge, sorting, reorganizing), commit the changes with a short descriptive message. Keep commits atomic: one commit per logical operation rather than batching unrelated changes. Do not push unless the user has configured a remote and asks to push.
--- title: "{Human-readable title}" source: "{URL, file path, or description of origin}" --- {Full extracted content. Preserve original structure where possible.} The filename carries the ID and date ({slug}--{YYYY-MM-DD}.md). No need to duplicate them in frontmatter. Rules: Preserve content faithfully. Do not editorialize. For YouTube transcripts, clean auto-sub artifacts but keep [HH:MM:SS] timestamps at paragraph boundaries. For URLs, strip navigation, ads, and boilerplate. Keep the article body.
--- category: {category-slug} description: "{One-sentence scope definition}" --- ## {Category Name} {1-3 sentences defining what belongs in this category and what does not. Be specific enough to resolve ambiguity when sorting.} ## Notes {Optional guidance on navigating this category, notable themes, or conventions specific to this domain.}
When the user provides a URL, YouTube link, file path, or content to capture: Step 1: Detect source type. YouTube URL: contains youtube.com/watch, youtu.be/, or youtube.com/shorts/ General URL: starts with http:// or https:// File path: exists on local filesystem Pasted text: inline content provided directly Step 2: Extract raw content. SourceMethodURLWebFetch(url, "Extract the main article content of this page as plain text")YouTubeBash: yt-dlp --write-auto-sub --sub-lang "en" --skip-download --print title --print description -o "/tmp/yt-%(id)s" "{url}" then Read the .vtt file and clean it (see transcript cleaning below)Local fileRead(filepath)AudioBash: whisper "{path}" --output_format txt if available; otherwise note as placeholderPasted textUse directly YouTube transcript cleaning: Remove VTT headers (WEBVTT, Kind:, Language:) Remove timestamp lines (lines matching XX:XX:XX.XXX --> XX:XX:XX.XXX) Remove duplicate consecutive lines Collapse blank lines Insert [HH:MM:SS] markers approximately every 60 seconds as paragraph breaks Clean up via Bash pipeline, then read and format the result Clean up temporary files in /tmp/ Step 3: Generate descriptive slug. Read the extracted content and produce a 3-6 word lowercase hyphenated slug. Examples: deep-work-productivity-strategies--2026-02-27 react-server-components-explained--2026-02-27 infant-sleep-training-methods--2026-02-27 Step 4: Check for duplicates. Grep across **/raw/*.md for the source URL in frontmatter. If found, warn the user and ask whether to overwrite or create a suffixed version. Step 5: Determine placement. Consult the knowledge index in agent memory. If an existing category is an obvious fit for this content (e.g., a workout video clearly belongs in fitness), place it directly in that category. If the fit is ambiguous or no category matches, place it in unsorted/. When in doubt, use unsorted/. Step 6: Write the raw file to {target}/raw/{slug}--{date}.md where {target} is either the chosen category or unsorted. Create the directory with mkdir -p if needed. Step 7: Generate the summary. Read the full raw content. Produce a summary following the template above. Step 8: Write the summary file to {target}/summary/{slug}--{date}.md. Step 9: Extract book references. Scan the raw content and summary for mentions of books โ titles, authors, "in his book...", "as described in...", ISBN references, etc. For each book found, append it to books-to-read.md at the knowledge base root (see Books to Read below). If the book is already listed, add this artifact's ID to its "Referenced By" column instead of creating a duplicate. Step 10: Update CHANGELOG.md. Append an "Added" entry under today's date. Step 11: Update the knowledge index in agent memory. If a new category was created or the unsorted count changed, update the index immediately. Step 12: Report to the user. Display the title, source, summary key points, file locations, the category it was placed in (or note that it's in unsorted/ and suggest sorting), and any books that were extracted.
When the user asks to sort knowledge or after adding new knowledge: Enumerate unsorted entries. Glob unsorted/summary/*.md. Read frontmatter for titles. Enumerate existing categories. Glob */_category.md (excluding unsorted/). Read each for scope. Propose categorization. For each unsorted entry: If an existing category fits, propose it. If no category fits, propose creating a new one with a slug and description. If the user specified a target, use that. Confirm with the user via AskUserQuestion. Present proposed moves as a list. Wait for confirmation. Execute moves. For each entry: a. Create target category directory structure if needed (mkdir -p {category}/raw {category}/summary). b. Write _category.md if this is a new category. c. Move raw file: Bash mv unsorted/raw/{filename} {category}/raw/{filename}. d. Move summary file: Bash mv unsorted/summary/{filename} {category}/summary/{filename}. Update CHANGELOG.md. Record moves and new categories. Update the knowledge index in agent memory. Reflect any new categories and the updated unsorted count. Report to the user.
Always confirm with the user before executing any reorganization. Split a Category Read all summary files in the source category. Propose which entries go to which new category. Confirm with user. Create new category directories and _category.md files. Move raw and summary files via Bash mv. Delete the old empty category directory via Bash rm -r after confirming it is empty. Update CHANGELOG.md. Update the knowledge index in agent memory. Merge Categories Create the target category if it does not exist. Move all raw and summary files from source categories to target. Handle filename collisions: append -2 suffix to both raw and summary filenames. Write or update target _category.md. Remove empty source directories. Update CHANGELOG.md. Update the knowledge index in agent memory. Rename a Category Rename directory via Bash mv. Update _category.md frontmatter. No changes needed in files โ filenames are unchanged, raw/summary pairing holds. Update CHANGELOG.md. Update the knowledge index in agent memory.
When the user asks a question, references a topic, or requests context that the knowledge base may contain: Consult the knowledge index. The memory index lists all categories with brief descriptions. Use it to identify which categories are likely relevant to the query. This narrows the search before touching any files. Locate relevant entries. Using whatever search capabilities the agent has: If the agent can search file contents (e.g., Grep, text search, embeddings), search within the identified categories' summary/ directories for keywords related to the query. If the agent can only read files, list the summary files in the relevant category directories and read their frontmatter (title) and content to find matches. If neither works, read the _category.md files for the relevant categories and follow their notes. Fall back to broader search across the full knowledge base only if category-scoped search yields nothing. Load into working context. Read the relevant summary files fully. If deeper detail is needed, read the corresponding raw file at ../raw/{same-filename}. The goal is to internalize the knowledge โ absorb it into the current conversation context, embedding, or working memory so it informs subsequent responses. Synthesize across entries. When multiple entries are relevant, connect them. Surface patterns, contradictions, or complementary perspectives across the loaded knowledge. Cite sources. When drawing on loaded knowledge, reference the entry by title and ID so the user can trace back to the original artifact. Proactive recall. When the knowledge index shows a relevant category exists, load and reference it without being asked. The knowledge base is an extension of memory โ if relevant knowledge is there, use it.
After completing any ability that writes or moves files (Abilities 1, 2, 3, 5), commit the changes to git if the knowledge base is a git repository. Use a short descriptive message. One commit per logical operation.
Suggest but never execute without user confirmation. Create a new category: 3+ entries in unsorted/ share a common theme not covered by existing categories. Split a category: 15-20+ entries with entries clearly clustering into 2+ distinct subtopics, or the _category.md description has become overly broad. Merge categories: Two categories with fewer than 3 entries each and significantly overlapping scopes. Rename a category: Name no longer reflects actual contents, or name is ambiguous. After adding knowledge, briefly note if reorganization might be warranted. Example: "Note: unsorted/ now has 5 entries about cooking. Consider creating a cooking category."
Before creating a new raw file, check if a file with the same filename already exists anywhere (Glob **/raw/{slug}--{date}.md). If so, warn the user and ask whether to overwrite or suffix.
If two artifacts about similar topics are captured on the same day and produce the same slug, append a numeric suffix: {slug}-2--{date}.md.
When the last entry leaves a category, ask the user whether to delete the empty category or keep it for future use.
If WebFetch fails, report the error and ask the user to provide content another way. If yt-dlp fails (no subtitles available), note this in the raw file and ask if the user can provide a transcript. If a move operation fails, do not update CHANGELOG until the move succeeds.
Plain, direct prose. No promotional language. Active voice. Specific facts over vague characterizations.
A flat index file at the knowledge base root that automatically captures book references found in any processed artifact.
# Books to Read | Title | Author | Referenced By | Date Added | |-------|--------|---------------|------------| | Deep Work | Cal Newport | deep-work-productivity--2026-02-27 | 2026-02-27 | | Atomic Habits | James Clear | habit-stacking-overview--2026-02-27, morning-routine-video--2026-02-28 | 2026-02-28 |
Create this file on first use (when the first book reference is found), not during initialization. Each row is one book. The "Referenced By" column contains a comma-separated list of artifact IDs that mention this book. When a book is already in the table and a new artifact references it, append the new artifact ID to the existing row's "Referenced By" column. Do not add a duplicate row. Extract whatever is available: title is required, author is best-effort. If the author isn't mentioned, leave the column as โ. Be conservative โ only extract clear book references, not vague mentions of "a book about X". This file is not tracked in the memory index. It is accessed explicitly when the user asks for book recommendations or reading lists.
On first-run, generate a brief README.md explaining the directory structure, file naming convention ({slug}--{date}.md), and basic usage (add, sort, reorganize, search, browse).
Writing, remixing, publishing, visual generation, and marketing content production.
Largest current source with strong distribution and engagement signals.