digest
Skill0 starsChanged 10 days agoMITView on GitHub
Import a raw document from staging into a knowledge base — privacy and copyright gates, filing, digest, index entry. Use when the user asks to import a document (นำเข้าเอกสาร, ย่อยเอกสาร), or files are waiting in staging.
33 other projects use a version of this.
Adopted by
Most recently taken up by
- zaynram/scholartheir own version, first seen Nov 8, 2026
- Jasenpan1987/multi-codetheir own version, first seen Nov 8, 2026
- zalogarcia/zalo-claude-code-setuptheir own version, first seen Nov 8, 2026
- PromptPotter/prompt-potter-optimizertheir own version, first seen Nov 8, 2026
- norrietaylor/distillerytheir own version, first seen Nov 1, 2026
- baobach/llm-wiki-vaulttheir own version, first seen Oct 25, 2026
Used with
Projects with it also use aeon-update, auto-workflow and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
aaronjmarsSkillaeon-update
Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.
Also in 4 projects that use this
aaronjmarsSkillauto-workflow
Two-mode aeon.yml workflow builder - analyze inspects URLs and emits a tiered, signal-verified skill-enablement plan plus an aeon.yml diff; enable flips slugs to enabled:true and opens a PR.
Also in 4 projects that use this
- +10 more from aaronjmars
Other versions33 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 33
- anthropics/knowledge-work-plugins★ 35ksame name
- aaronjmars/aeon★ 789same name
- aeonfun/aeon★ 789same name
- jexchan/dailyup-second-brain-starter★ 33same name
- norrietaylor/distillery★ 25same name
- wrg32786/aigent-os★ 25same name
- aaronjmars/miroshark-aeon★ 20same name
- MiroShark/miroshark-aeon★ 20same name
- Treasoni/kaoyan_skills★ 19same name
- alxyrgin/secondbrain_ai★ 17same name
23 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- adopt-poolSkill
- analyze-resultsSkill
- assessment-layoutSkill
- audit-brainSkill
- brain-anatomySkill
- critique-mcqSkill
- critique-thesisSkill
- design-courseSkill
- grill-pointSkill
- init-brainSkill
- item-defectsSkill
- learning-outcomesSkill
- make-examSkill
- make-practicalSkill
- new-subjectSkill
- thai-office-filesSkill
- update-portfolioSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 29 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
Otherwise skim extracted text first: if a text-extraction tool happens to be installed (pdftotext -raw), and read pages directly only where …
“Common” is measured: kept by at least 6 in 10 of the 29 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
digest — import raw documents into the knowledge base
This skill is an ordered checklist: each step opens when the one before it has produced its result. It carries the process only — everything project-specific (folder layout, naming rules, templates) is discovered from the host project at runtime.
Whenever a step puts more than one decision to the user, it asks them in the round shape the grill-point skill gives in its step 3.
Steps
- Discover the project's conventions — read the host project's agent instructions file (
AGENTS.md, orCLAUDE.md) and inspect the existing structure: where imported sources live, where digests live, whether an index/registry and a digest template exist. Explicit import rules in the host project override the generic defaults below. If no staging folder is defined, ask the user which folder is the inbox. Then show the user the plan for this run — its steps by number with a 🛑 on each one that stops for the user, then one line of what this step found and one of what it could not find, labelled in the user's language — and wait for the user's go-ahead, or take the dispatch as the go-ahead when this run was sent to work without the user, as a sub-agent; name each later step by its number as it starts. - Survey the staging area — list the waiting files (commonly
temp/orinbox/; typically untracked in git). - Read the actual content before any decision — judge the document by what is inside it, never by its filename. Reading a scanned or image-heavy source puts its pages into the working session, which then carries them for everything that follows; on a long document that can cost more than the rest of the import put together. Take the cheapest path that actually works, in this order:
- Read it in an isolated worker if your agent has one — a sub-agent or equivalent reads the document and returns the summary, so the pages never enter this session at all. Ask it for what steps 4-10 need: subject matter, whether personal data appears, whether it is an original record, copyright status, key content.
- Otherwise skim extracted text first if a text-extraction tool happens to be installed (
pdftotext -raw), and read pages directly only where extraction comes back empty — which is what scanned documents do. - Otherwise read the document directly with your own file-reading capability, which handles scanned pages as well as digital text. This path always works; it is simply the expensive one, so it is the fallback rather than the default.
- Pass two gates before filing anything:
- Privacy — does the document hold personal data as its content: records about identifiable people who did not publish them — students, patients, clients, research participants, staff — such as names on a roster, ID numbers, individual scores, contact details, or case notes? If yes: stop, notify the user, do not digest. The people a published work names as its own authors, editors, or cited sources are its byline, not its content; a paper is not blocked for carrying an author list, an affiliation, or a corresponding-author email. Where a document holds both, the gate fires on the personal data it holds, whatever its byline says.
- Original official records — is this an original organizational record rather than reference material? Originals belong in the organization's system of record, not a knowledge base; file only a digest/summary here, and ask the user where the original should go.
- Choose the destination by what the content is, using the structure discovered in step 1 — e.g. a repo split by role might route teaching material, research, and admin work to different trees. If the content fits no discovered destination, or fits more than one, ask the user instead of guessing.
- Check for waiting gaps — before creating a new file, search the destination's README/index for placeholders or "not yet written" markers; a new document often fills a gap that already exists.
- Decide copyright before moving the file:
- The user's own writing, public and official publications → the source file may be stored.
- Someone else's work carrying a licence that permits redistribution — Creative Commons, an open-access statement, a public-domain dedication, an open-source licence → the source file may be stored, and the licence is recorded beside it, so a later reader can tell why it was allowed to stay.
- Everything else a third party holds rights over — textbooks, paywalled articles, purchased material → write a digest with a full citation; the source file is not stored.
- Unsure, or a licence you cannot read off the document → ask the user.
- Rename meaningfully — lowercase, hyphen-separated, with issuing org and year (e.g.
who-ai-ethics-guidance-2024.pdf), unless the project defines its own naming rule. - Leave nothing behind in staging silently — when the source file was stored (step 7), move it (don't copy): remove the original from staging once filing succeeds. When only a digest was written (file not stored), ask the user what to do with the original — delete it from staging, or hand it back for them to file elsewhere.
- Write the digest and register it — follow the project's digest template if one exists; update the README/index to point at the new file. If the digest closes a gap found in step 6, update the gap's status in the same edit. Before writing an unchecked mark — whatever label the host project gives a claim not yet checked against the source it rests on — and for each one already present in a file this run edits, open the primary source the claim rests on, not the document being digested: the cited paper's abstract or its DOI record, the repository, the dataset the document names, recomputed. Where that source settles the claim, write the claim as the source gives it, citing it. A mark left standing names the source tried and what stopped it — the address that refused the fetch, the tool this machine lacks — or, where no source can be reached from here, the source it would need.
- Verify before finishing — run the project's check script where one exists, then confirm: staging area empty (or leftovers reported to the user), every new file linked from an index, no broken links introduced, every unchecked mark in the files this run wrote or edited naming the source tried and what stopped it, or the source it would need.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

