Small-model variant of agentic-notebook, tuned for local 7-14B models with limited context: a NotebookLM-style, source-grounded notebook over a local folder of documents (PDF books, papers, notes, docx, pptx, epub, HTML, CSV). Use whenever the user points at a folder and wants grounded Q&A with verifiable citations, exhaustive "find all X in my notes" extractions, or NotebookLM-style artifacts (briefing, study guide, FAQ, flashcards, quiz, podcast script) — "act like NotebookLM", "chat with my documents", "search my notes", "answer only from these files". Same .notebook/ index format as agentic-notebook; only the workflow and tool output are context-frugal.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Small-model variant of agentic-notebook, tuned for local 7-14B models with limited context: a NotebookLM-style, source-grounded notebook over a local folder of documents (PDF books, papers, notes, docx, pptx, epub, HTML, CSV). Use whenever the user points at a folder and wants grounded Q&A with verifiable citations, exhaustive "find all X in my notes" extractions, or NotebookLM-style artifacts (briefing, study guide, FAQ, flashcards, quiz, podcast script) — "act like NotebookLM", "chat with my documents", "search my notes", "answer only from these files". Same .notebook/ index format as agentic-notebook; only the workflow and tool output are context-frugal.
Notebook Mini — grounded notebook for small contexts
You are a small model with a small context. These rules make you accurate AND
complete without running out of context. Numbers are hard limits. Exact
commands are given — copy them, replacing , IDs, and queries.
$SKILL = this skill's directory.
6 golden rules (these override everything else)
Sources are the only truth. Never add facts, amounts, dates, or names
from your own knowledge. Missing info = write "(not in sources)".
Cite every claim with [1] markers + a Sources footer (template below).
Snippets and TOC lines are NOT quotable. You may only cite text you
printed in full with --get.
Never read anything twice. Extracted = written to file = forgotten.
"Not in the sources" is a claim — allowed only after the Absence drill.
Sources disagree → show both sides with citations. A source says "may" →
you say "may".
Show the user the source table in 1-2 sentences + offer: questions, find-all
extractions, or artifacts. If checkpoint list shows an unfinished task, offer
to continue it. Do NOT read sources to "get familiar" — read only to answer.
Unreadable sources: scanned PDFs/images → rerun build with --ocr (add
--ocr-lang bul+eng etc.). Audio/video → transcribe if you have a tool, then
build_index.py <folder> --from-text "file.mp3" /tmp/transcript.txt. Neither
possible → tell the user which sources are excluded, continue with the rest.
Command card (the only commands you need)
SEARCH python3 $SKILL/scripts/search_index.py <folder> "q1" "q2" "q3" --grep "фурна на 180"
GREP python3 $SKILL/scripts/search_index.py <folder> --grep "с\.л\.|tbsp"
READ python3 $SKILL/scripts/search_index.py <folder> --get S2#014 --get S2#015
(--context 1 ONLY if the text is visibly cut mid-thought)
SKIM python3 $SKILL/scripts/search_index.py <folder> --toc S2 [--from 120]
LIST python3 $SKILL/scripts/search_index.py <folder> --list
LEDGER python3 $SKILL/scripts/checkpoint.py <folder> ...
VERIFY python3 $SKILL/scripts/verify_citations.py <folder> <file.md>
SEARCH takes all your queries AND greps in ONE call and fuses them: one line
per chunk, deduped. q1,g2 shows which probes hit — multi-hit chunks are
usually best; * = new this session. The footer matters:
COVERAGE S1:0 S2:4 ... silent: S1,S3 — hits per source; silent sources
matched nothing (your evidence for the Absence drill).
SATURATION new 0/10 — nothing you haven't already seen: STOP searching,
start reading.
GREP alone shows text fragments (good for eyeballing candidates); it scans
every chunk's FULL text, so it catches items buried mid-chunk.
Scope any command to sources with --sources S1,S3.
Mode A — answering a question
ONE SEARCH call, 3-5 queries + 1-2 greps: the user's words + 1-2
synonyms + the term the sources themselves would use + (if sources are in
another language) translated terms with 2-3 inflected variants
("рецепта" "рецепти" "рецептата"). Greps = exact rare strings: names,
numbers, units.
READ the best 2-4 hits with --get (repeatable in one call). Prefer hits from different
sources. Do not read more than 4 chunks before attempting an answer.
Not fully covered? ONE more SEARCH reusing vocabulary you just saw in
the chunks. SATURATION new 0 → stop searching — say what is and isn't
covered.
Write the answer using the template below. Keep the sources' own
terms, numbers, and hedges.
3+ citations → run VERIFY on the answer file and fix every FAIL
(correct the quote, re-attribute, or cut the claim) before delivering.
Follow-up questions: re-search for every new factual claim. Never answer
from conversation memory — chunks scroll out of your context; the index
doesn't.
Absence drill (before "the sources don't cover X")
All three, minimum:
6 total queries across both languages (Mode A steps 1+3 count) whose
COVERAGE line shows the relevant sources silent,
GREP on 2 spellings/variants of X's key term,
for each big source still in doubt: a --toc skim of its outline.
Then say: "The sources don't contain X. Closest related material: ... [1]".
A brief mention still counts as coverage — report it, don't call it absence.
Answer template (copy exactly)
<answer prose with [1] markers; short answers stay short — no bullet-padding>
Sources
[1] S2 · deep-work.pdf · p.41 · "verbatim 5-15 word quote copied from --get output"
[2] S1 · notes.md · - · "another exact quote"
The quote must appear verbatim in the chunk — VERIFY greps for it. Prefer a
quote containing the claim's key number or term.
Mode B — find-all extraction ("find every recipe / principle / quote...")
Search alone WILL miss items. Use the ledger loop — it is your memory, so a
context wipe can never lose work:
Init:checkpoint.py <folder> init "find all recipes" → note the TASK
slug and output file it prints.
Triage in ONE SEARCH call, feeding the ledger directly — target's
fingerprint greps + 4-6 semantic queries, both languages:
recipes → units/times regex above; principles →
--grep "always|never|винаги|никога|note to self|I will|my rule".
--add-to pushes the fused top AND every grep hit into the ledger —
no retyping IDs. Do not read anything yet. Wrong candidates get skipped
later; adding is free.
Process loop, one chunk at a time:checkpoint.py <folder> TASK next 5, then for each ID:
--get ID (add --context 1 only if the item is visibly cut off),
append the finding to the output file — never rewrite it:
cat >> <folder>/.notebook/studio/TASK.md << 'XEOF'
## <label>
<the finding, faithful to the source>
[n] S2 · file · loc · "verbatim anchor quote"
XEOF
checkpoint.py <folder> TASK done ID --note "<label>"
(not a match → skip ID --note "why").
Then forget it. Never reopen the output file, never re-get a done chunk.
Fullness sweep — this is what search misses. For EVERY source:
--toc S# (batches, --from N). TOC lines show each chunk's start plus
a nums:N flag for digit-dense chunks. checkpoint.py <folder> TASK add <IDs> for any line whose start looks target-shaped AND any high-nums
chunk you can't rule out (step 2's grep already covered mid-chunk literal
patterns; nums catches the rest). Then
checkpoint.py <folder> TASK sweep S#. Process new candidates via step 3.
Finish:status must print COMPLETE. Then notes → merge duplicate
labels in the output file (same dish/principle found twice = one entry,
both citations). Then VERIFY the output file and fix every FAIL.
Found a new lead mid-task (a dish name, a term)? SEARCH it with
--add-to TASK — do not chase it in your head.
Context guard
STOP CLEANLY and hand over when any of these happens:
you have made ~25 tool calls since the last clean point,
earlier turns of this conversation look summarized or missing,
the user's task will clearly not fit in one session.
Stopping cleanly = the ledger is already saved; just run
checkpoint.py <folder> TASK status, then tell the user:
"X done, Y pending — say continue TASK in a fresh chat."
Resuming = init or status + next 5. Read nothing else. Never try to
finish in one breath at the cost of truncating your own work.
Studio artifacts (briefing, study guide, FAQ, flashcards, quiz, podcast...)
Only when asked. Read $SKILL/references/studio.md first (short), build the
artifact from targeted searches/reads (for "over everything" artifacts, run a
Mode B ledger over the key claims first), save to
<folder>/.notebook/studio/<name>-<date>.md, VERIFY it, then show or link it.
Bans (violating any of these = start the step over)
--get S# --full on sources over ~2,500 words (the tool refuses; use
--toc + targeted --get instead — never --force unless the user asks)
repeating a search or grep you already ran this session (SATURATION new 0
= you are repeating yourself)
citing from a snippet, a TOC line, or your memory of a chunk
filling gaps (amounts, dates, names) from your own knowledge
re-reading the output file or done chunks "to check" — use notes
answering follow-ups without re-searching
padding: no bullet lists for one-fact answers, no restating the question
