skills/ingest-inbox-item/SKILL.md
--- name: ingest-inbox-item description: Ingests a single file from the substacker inbox/ into corpus/seeds/ as a normalized markdown seed with full frontmatter. Orchestrates format normalization, topic tagging, intuition-density scoring, dedupe, changelog, ledger update, and inbox-file move to .processed/. Use when the user drops raw material into inbox/ and runs /ingest, at session start, or whenever a single inbox file needs to become an indexed seed. Trigger keywords: ingest, inbox, new note
npx skillsauth add lyndonkl/claude skills/ingest-inbox-itemInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Related skills: Uses normalize-format, tag-by-topic, score-intuition-density, dedupe-against-corpus, update-topic-ledger. Called by the Librarian agent for each file in inbox/.
Copy this checklist and track progress per file:
Ingest one inbox file:
- [ ] Step 0: Compute sha256 of file body; check .librarian-state.json for duplicate fingerprint
- [ ] Step 1: Invoke normalize-format → body + partial frontmatter
- [ ] Step 2: Invoke tag-by-topic → topics + pending_tags
- [ ] Step 3: Invoke score-intuition-density → score + signals
- [ ] Step 4: Invoke dedupe-against-corpus → CREATE | LINK | SKIPPED
- [ ] Step 5: If LINK, backlink matched seeds' related_seeds field
- [ ] Step 6: Write seed file with full frontmatter; status=seed; manual_edits=false
- [ ] Step 7: Append one line to corpus/seeds/.changelog.md
- [ ] Step 8: Invoke update-topic-ledger for each topic tag
- [ ] Step 9: mv inbox file → inbox/.processed/
- [ ] Step 10: Update .librarian-state.json with the fingerprint
Step 0: Hash the body (not frontmatter). If the fingerprint is in .librarian-state.json, exit with SKIPPED (already ingested: <seed-id>). Idempotency is a feature.
Step 1: normalize-format handles all supported formats (plain markdown, .jsonl Claude Code sessions, .json Claude.ai exports, Readwise exports, transcripts with speaker labels, link captures). Returns one or more {body, partial_frontmatter} pairs — multi-chunk outputs possible for long transcripts.
Step 2: tag-by-topic proposes 1–4 tags from the controlled vocabulary. If none match, it logs to topic-ledger.md#pending-tags and still assigns the closest existing tag.
Step 3: score-intuition-density computes 0–10 from 8 explicit signals. Auditable.
Step 4: dedupe-against-corpus returns one of SKIPPED (exact fingerprint match — exit), LINK (near-match — proceed but write related_seeds), or CREATE (no match).
Step 6: Write the seed file. Location: corpus/seeds/{id}.md where id = YYYY-MM-DD-slugified-title. If a seed with that id already exists, append -v2 rather than overwrite.
Step 7: Changelog format: YYYY-MM-DDThh:mm | ADDED | {seed-id} | from {original_path} | density={N} | topics={comma-list}
---
id: 2026-04-23-dropout-as-ensemble-thinned-networks
title: "Dropout as ensemble over thinned networks"
created: 2026-04-21T09:14:00-07:00
source:
type: inbox-note
original_path: inbox/2026-04-21-dropout-as-ensemble.md
ingested_at: 2026-04-23T14:32:01-07:00
fingerprint: sha256:7c1d...
topics: [regularization, ensembling, dropout]
intuition_density:
score: 8
signals: [analogy_present, concrete_worked_example, counterfactual_offered, reframe_against_default, biology_to_ai]
status: seed
provenance:
author: kushal
confidence: owned
links:
related_seeds: [2026-03-11-l2-as-gaussian-prior]
parent_source: null
section_affinity: [agent-workshop]
word_count: 87
manual_edits: false
---
[body: preserved verbatim from normalize-format output]
Input: inbox/2026-04-21-dropout-as-ensemble.md (87 words, plain markdown, user's own note).
Run:
[regularization, ensembling, dropout]; dropout new → logged to pending-tags.2026-03-11-l2-as-gaussian-prior at Jaccard 0.32 (below 0.5 threshold) → CREATE (not LINK).corpus/seeds/2026-04-21-dropout-as-ensemble-thinned-networks.md with full frontmatter.2026-04-23T14:32 | ADDED | 2026-04-21-dropout-as-ensemble-thinned-networks | from inbox/2026-04-21-dropout-as-ensemble.md | density=8 | topics=regularization,ensembling,dropoutregularization seeds 3→4, temperature→hot; ensembling 1→2; dropout new row added.mv inbox/2026-04-21-dropout-as-ensemble.md inbox/.processed/Output: one seed file, one changelog line, three ledger updates, one moved file.
normalize-format fails, the inbox file stays put (not moved) and the changelog records ERROR | <file> | <reason>. No partial ingests.id exists, append -v2; never overwrite.inbox/.processed/ or inbox/.trash/.dedupe-against-corpus returns SKIPPED, move the inbox file to .processed/ with a renamed suffix -duplicate-of-{matched-seed-id} and exit — don't re-write the matched seed.testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.