Fully autonomous end-to-end ingestion of a SINGLE source (book, paper, article, transcript) into the knowledge base - prepare to markdown, extract insights against the live index, refresh the index, auto-link to existing knowledge, and changelog. One source per invocation.
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SKILL.md
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name
ingest-source
description
Fully autonomous end-to-end ingestion of a SINGLE source (book, paper, article, transcript) into the knowledge base - prepare to markdown, extract insights against the live index, refresh the index, auto-link to existing knowledge, and changelog. One source per invocation.
Fully autonomous pipeline that takes one source from raw file to integrated, connected knowledge. Each invocation handles a single source; drive a whole corpus by invoking once per source (e.g. via /loop or a shell loop over a file list), then run the corpus-level finalize steps once at the end.
ultrathink
Purpose
The unit of work for building a knowledge base from a book corpus. One source in → deduplicated, epistemically-classified notes out, embedded in the graph and linked to existing knowledge, with every auto-action logged for post-hoc review.
The design principle (established in the architecture discussion): insights are extracted against the current KB, not in a vacuum — retrieval-augmented extraction. The index is the KB's queryable state, so it must be refreshed before connection discovery, and refreshed again on the next source so each source builds on the last.
Configuration (safety rails that replace human gates)
Knob
Default
Purpose
AUTO_LINK_THRESHOLD
0.75
Only auto-write links at or above this cosine similarity. Below → logged as review candidates, not written.
LINKS_PER_NOTE
5
Max auto-links written per new note (top-k). Caps combinatorial blowup.
MUTATE_EXISTING_NOTES
false
If false, only the NEW notes get edited (links written FROM new → existing). Existing/hub notes are never mutated by auto-linking.
REJECT_ON_TIER
rejected
If the extractor tiers the source rejected (content-farm / regurgitated), abort and notify.
These are the autonomous substitute for the two approval gates a gated version would have. See Architecture Note for why.
Parse $ARGUMENTS: first token = source path/URL, optional remainder = session name. Derive a <slug> and, if no session name given, derive one from the source title (e.g. "<Book Title>").
If a checkpoint exists, resume from the next phase rather than restarting (re-running earlier phases is safe — they are idempotent — but wasteful for large books).
Scope routing (book vs. non-book). Decide the destination scope now:
Source is…
Destination dir
Scope token (for read-scope mounts)
a book — .epub / .pdf / .mobi / .azw3, or --book is passed
Brain/Books/<book-slug>/
Books/<book-slug> (its own pluggable scope)
anything else — paper / article / web / transcript / YouTube
Brain/Document Insights/<session>/
document-insights (the shared bucket)
A book gets its own scope (Books/<slug>): non-core (never pollutes the core fingerprint or trains q-values), cognitive (its gems can graduate to core via the human /graduate-insights act), and pluggable (mount with BRAIN_READ_SCOPE=core,Books/<slug> or the whole shelf with core,Books). Set TARGET_DIR and BOOK_SLUG accordingly and use them in Steps 2/4/6 below.
Step 1: Prepare source → markdown
Detect format by extension/URL and convert to markdown:
Input
Action
.epub
Skill: epub-chapter-extractor → per-chapter .md
.pdf / .mobi / .azw3
Skill: multi-format-book-extractor
YouTube URL
Skill: get-youtube-transcript
.md / .txt
use directly (no conversion)
Write prepared markdown into resources/ingest-workspace/<slug>/.
Checkpoint: phase=prepared.
Step 2: Extract insights (against the live index)
Spawn the extractor directly (autonomous — skip the interactive insight-interview suggestion).
For a book (Step-0 routing → Brain/Books/<book-slug>/), invoke in Book Mode:
Task(
subagent_type="document-insight-extractor",
prompt="BOOK MODE. Extract insights from <prepared markdown path(s)> into the book scope
Brain/Books/<book-slug>/ (NOT Document Insights). Follow your Book Mode doctrine:
mine like a scientist THROUGH THE KB LENS (contextualize against the CURRENT index
as a GATE); capture ONLY genuine frameworks / mental models / methods / evidenced
contrarian claims; REJECT narrative, anecdote, biography, he-said-she-said, and
restatements of the obvious or of concepts the KB already holds; MECE + atomic
(one idea per note, no overlap within the book scope); expected shape ~10 core
concepts + up to ~20 supporting nodes as REFERENCE POINTS not caps; prefer
statistically-meaningful research (N, effect size, replication, year) over one-off
examples and record dates. Apply Gate 1 source-tiering and provenance: encountered;
SEARCH FOR DUPLICATES (mount BRAIN_READ_SCOPE=core,Books,document-insights) and
merge/link rather than re-create; create the _book.md literature hub; update the
changelog. Return: list of new note file paths, total count, epistemic split,
assigned source-tier, and the book scope path."
)
For non-books (paper / article / web → Brain/Document Insights/<session>/):
Task(
subagent_type="document-insight-extractor",
prompt="Extract insights from <prepared markdown path(s)> into session '<session name>'.
Follow your mandatory workflow: contextualize against the CURRENT index,
apply Gate 1 source-tiering and Gate 2 provenance (provenance: encountered),
epistemically classify each note, SEARCH FOR DUPLICATES and merge/link rather
than re-create, write notes to Brain/Document Insights/<session>/, update the
session changelog. Return: list of new note file paths, total count,
epistemic split, and the assigned source-tier."
)
Safety rail (replaces Gate 1): if the returned source-tier is REJECT_ON_TIER, abort, log the rejection to the changelog, and notify. Low-confidence/speculative notes are kept but flagged in the changelog, never silently dropped.
Capture the new note paths — only these notes are processed downstream (this keeps connection discovery linear, not all-vs-all).
Checkpoint: phase=extracted (persist the note list).
Step 3: Refresh the index
The FAISS index does not auto-update, and connection discovery looks notes up by title in the index — so new notes are invisible until reindexed. Refresh now, before discovery:
(This is exactly /refresh-index; one full rebuild per source also sweeps in the prior source's auto-links.)
Checkpoint: phase=indexed.
Step 4: Discover connections (bounded, new → existing)
For each new note only, query its neighbors. Use a wide read-scope so a new
note resolves and links against existing non-core neighbors (core-only would miss
them once scope enforcement is on). Mount the destination scope: for a book use
core,Books (so it links to core and across the whole shelf); for a non-book use
core,document-insights:
Collect top-LINKS_PER_NOTE connections per note with similarity scores and BDG edge type. Do not run all-pairs or hub/bridge sweeps here — that is the combinatorial explosion this step is designed to avoid. New→existing is linear in the source's note count.
Checkpoint: phase=connections-discovered (persist the connection report).
Step 5: Auto-write links (the gated step, made safe)
For each new note, write wiki-links to its qualifying connections:
Threshold: only connections with similarity ≥ AUTO_LINK_THRESHOLD.
Cap: at most LINKS_PER_NOTE per note.
Idempotent: skip if the [[link]] already exists in the note.
Labeled + reversible: append under a clearly marked section in the NEW note only:
## Related (auto-linked by /ingest-source)
<!-- AI-discovered via semantic similarity; review critically. similarity ≠ conceptual validity -->
- [[Target Note]] — 0.82, derives-from
Never mutate existing notes when MUTATE_EXISTING_NOTES=false — links go FROM the fresh notes outward, so hub notes are never polluted and every auto-edit is confined to notes created this run (trivially reversible by deleting the session folder).
Below-threshold connections → recorded in the changelog as "candidates for human review," not written.
Tension preservation: if a high-similarity connection is to a note with an opposing claim (BDG edge type tension, or the extractor flagged a contradiction), DO NOT collapse or dedupe it away. Record it in the changelog under "Tension candidates" for the manual /detect-tensions pass. Never auto-resolve a contradiction.
Use Edit on the new note files (their paths are known from Step 2).
Checkpoint: phase=linked.
Print a summary and the after-the-corpus reminder (these are manual — judgment work, per the architecture):
Source ingested. After the LAST source in the corpus, run once:
/detect-tensions → /coherence-sweep → /graduate-insights
(cross-source contradictions, structural health, and canonicalization into permanent notes).
Notify on completion. On any unrecoverable failure, notify with the failed phase and the checkpoint path so the run can be resumed. Then clear the checkpoint (phase=done).
Outputs
Deduplicated, epistemically-classified notes in the destination scope — Brain/Books/<book-slug>/ (books, plus a _book.md literature hub) or Brain/Document Insights/<session>/ (non-books)
Refreshed FAISS index + BDG enrichments
AI-labeled wiki-links from the new notes into the existing graph
Log, notify, abort. Markdown/text passthrough always works as fallback.
Source tiered rejected
Log rejection to changelog, notify, abort — do not extract.
Extractor returns 0 notes
Log "no extractable insights," notify, mark ledger empty, exit.
run_index.sh fails
Abort before linking (linking on a stale index would mislink). Resume from phase=extracted.
BDG bootstrap fails
Non-critical — LBS links still work. Log and continue without edge-type labels.
Connection query fails for a note
Log, skip that note, continue with the rest.
Interrupted mid-run (45-min rule)
Re-invoke with same source; the .checkpoint resumes from the next phase.
Autonomous Validation Checklist
No approval gates — none present; the two judgment points are replaced by threshold + cap + label + audit rails.
No human decision points — runs unattended start to finish.
Complete error handling — every phase has a recovery path.
Notifications on failure — failed phase + checkpoint path reported.
Under 45 minutes — one source per invocation; extractor chunks large files internally; refresh cost equals the daily scheduled refresh. Checkpointing resumes if exceeded.
Idempotent / safe to retry — extractor dedups; refresh is idempotent; link-writing checks for existing links; resume via checkpoint.
Single-task scope — exactly one source per invocation; the scheduler / loop drives the corpus.
Architecture Note (deliberate deviation)
ARCHITECTURE.md and the autonomy principle state: "Automate measurement and maintenance. Never automate creation or judgment," and list connection-writing as manual-only. This skill deliberately crosses that line for connection-writing because the use case is bulk ingestion of a book corpus, where per-source human gating does not scale.
The deviation is bounded, not unbounded. The architecture's real concern is un-auditable, irreversible auto-judgment. This skill substitutes:
a similarity threshold + per-note cap for the "which links are valid" judgment,
AI-labeled, new-notes-only edits so every auto-action is transparent and reversible (delete the session folder),
a changelog audit trail enabling review-after instead of approve-before,
tension-preservation so contradictions are surfaced, never auto-collapsed.
This mirrors how /auto-discovery and the incubation loop already auto-write AI-labeled content to the graph. What remains strictly manual — and is intentionally NOT in this skill — is the corpus-level judgment work: /detect-tensions, the final /coherence-sweep, and canonicalization via /graduate-insights. If you want the conservative posture instead, set MUTATE_EXISTING_NOTES=false (already default), raise AUTO_LINK_THRESHOLD, or fork a gated variant that stops at Step 4 and emits the connection report for human approval.
Self-Improvement
After completing this skill's primary task, consider tactical improvements:
Review execution: Were there friction points, unclear steps, or inefficiencies (e.g. extractor timeouts, mislinks, threshold too loose/tight)?
Identify improvements: Could error handling, step ordering, the safety-rail defaults, or instructions be clearer?
Scope check: Only tactical/execution changes — NOT changes to core purpose (the per-source pipeline shape) or the architecture-deviation rationale.
