ingest-source

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.
automation	autonomous
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep","Task","Skill"]
user-invocable	true
argument-hint	<source path or URL> [session name]
effort	high
metadata	{"version":"1.0","created":"2026-06-03T00:00:00.000Z","author":"Cornelius"}

Ingest Source

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.

State Dependencies

Source	Location	Read	Write	Description
Source file	arg path / URL	✓		Book, paper, article, transcript, or YouTube URL
Working markdown	resources/ingest-workspace/<slug>/	✓	✓	Extracted/prepared markdown + checkpoint
Book scope (books)	Brain/Books/<book-slug>/	✓	✓	Per-book scope: notes + _book.md hub land here
Document Insights (non-books)	Brain/Document Insights/<session>/	✓	✓	Papers / articles / web extracted notes land here
FAISS index + BDG	resources/local-brain-search/, resources/brain-graph/	✓	✓	Refreshed mid-pipeline
Changelogs	Brain/05-Meta/Changelogs/		✓	Per-source ingestion report
Ingest ledger	resources/ingest-workspace/INGEST-LEDGER.md	✓	✓	Corpus progress: which sources done
Checkpoint	resources/ingest-workspace/<slug>/.checkpoint	✓	✓	Resume marker for the 45-min rule

Prerequisites

• Local Brain Search installed (resources/local-brain-search/run_index.sh, run_connections.sh)
• Brain Dependency Graph engine (resources/brain-graph/run_brain_graph.sh)
• Extractor agents available: document-insight-extractor
• Source-prep skills available: epub-chapter-extractor, multi-format-book-extractor, get-youtube-transcript

Process

Step 0: Read inputs and checkpoint

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>").

date '+%Y-%m-%d'
test -f resources/ingest-workspace/<slug>/.checkpoint && cat resources/ingest-workspace/<slug>/.checkpoint || echo "phase=start"

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:

resources/local-brain-search/run_index.sh
resources/brain-graph/run_brain_graph.sh bootstrap --force
resources/local-brain-search/run_connections.sh --stats --json   # verify count grew

(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:

# book:
BRAIN_READ_SCOPE=core,Books resources/local-brain-search/run_connections.sh "<New Note Title>" --json
# non-book:
BRAIN_READ_SCOPE=core,document-insights resources/local-brain-search/run_connections.sh "<New Note Title>" --json
resources/brain-graph/run_brain_graph.sh inspect "<New Note Title>" --json   # edge type + lifecycle

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.

Step 6: Changelog

Write Brain/05-Meta/Changelogs/CHANGELOG - Source Ingestion <session> YYYY-MM-DD.md:

## Source Ingestion: <session> — YYYY-MM-DD

**Source:** <path/URL>  |  **Tier:** <primary|credible-interpreter>  |  **Format:** <epub/pdf/...>
**Notes created:** N  (confirmed: X · theoretical: Y · speculative: Z)

### Auto-linked (≥ AUTO_LINK_THRESHOLD)
- [[New Note]] → [[Existing]] (0.82, derives-from)

### Review candidates (below threshold)
- [[New Note]] ~ [[Existing]] (0.71)

### Tension candidates (DO NOT auto-resolve — for /detect-tensions)
- [[New Note]] vs [[Existing]] — opposing claims at 0.78

### Isolated notes (0 connections ≥ threshold) — priority for human attention
- [[New Note]]

Checkpoint: phase=changelogged.

Final Step: Write ledger + state, notify

Append to resources/ingest-workspace/INGEST-LEDGER.md:

| YYYY-MM-DD | <session> | <source> | N notes | done |

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
• Per-source ingestion changelog (auto-links, review candidates, tension candidates, isolated notes)
• Updated corpus ledger entry

Error Recovery

Failure	Recovery
Source-prep fails (unsupported format)	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.
• Apply improvement (if identified):
  • Edit this SKILL.md with the specific improvement
  • Keep changes minimal and focused
• Version control (if in a git repository):
  • Stage: git add .claude/skills/ingest-source/SKILL.md
  • Commit: git commit -m "refactor(ingest-source): <brief improvement description>"