LLM Knowledge Bases

---

name: llm-knowledge-bases description: Inspired by a public workflow shared by Andrej Karpathy (@karpathy). Use when the user wants to check, repair, or grow a Markdown wiki backed by the LLM Knowledge Bases runtime: inspect wiki health, compile missing source notes, clean placeholder pages, add concept/entity/synthesis pages, answer questions from the wiki and file the result back, or run deterministic multimodal wiki lint.

---

LLM Knowledge Bases

Use this skill to operate a Vault that is managed by the LLM Knowledge Bases runtime.

The operating model is:

raw/ stores captured source material from the outside world

wiki/sources/ stores compiled source notes

wiki/outputs/ stores archived answer notes

wiki/concepts/ stores durable concept pages

wiki/entities/ stores durable entity pages

wiki/syntheses/ stores cross-source synthesis pages

wiki/_indexes/ stores generated collection indexes

wiki/index.md stores the generated home index

wiki/log.md stores the generated run log page

.llm-kb/ stores runtime state

.llm-kb/representations/ stores runtime-managed OCR, vision, metadata, and profiling artifacts for non-text raw assets

The runtime owns Vault I/O. The agent owns understanding, synthesis, linking, and deciding which pages the wiki should gain or improve.

Important Model

Treat the Vault as runtime-managed.

Use kb_* tools for all Vault reads and writes.

Never modify files under raw/.

Never write directly into wiki/ or .llm-kb/representations/ with generic file tools.

Never fabricate IDs, canonical paths, raw hashes, representation paths, or source_refs.

For text and structured-data raw files, use the direct source compile path.

For PDF and image raw files, use the representation-first path: bundle the asset context, create any missing representations, then compile the source note.

When a source note summarizes a PDF or image, keep asset_paths accurate and include visible review notes, usually # Visual Notes, whenever the source depends on a human or model review pass outside the stored representation files.

The goal is not only to answer questions.

The goal is to leave the wiki better structured after each meaningful interaction.

Required Tools

kb_status

kb_list_raw

kb_read_raw

kb_get_raw_asset

kb_prepare_source

kb_prepare_source_bundle

kb_prepare_representation

kb_upsert_representation

kb_read_representations

kb_upsert_source_note

kb_prepare_output

kb_upsert_output

kb_prepare_derived_note

kb_upsert_derived_note

kb_search

kb_read_notes

kb_map_gaps

kb_promote_gap

kb_repair_source_ids

kb_rebuild_indexes

kb_lint

If these tools are unavailable, say so clearly instead of pretending the workflow can proceed.

Natural-Language Entry Points

Users do not need to name canonical actions. Map short natural-language requests to the closest workflow. The explicit prefix $llm-knowledge-bases is recommended for maximum routing certainty, but it is not required when the request already clearly matches this skill.

check my wiki, look over my knowledge base, 检查我的 wiki, 总览检查:

run kb_status plus kb_lint, report counts, the top issues, and the best next step

fill missing source notes, continue the backlog, 补缺失 source notes, 继续推进这份库:

use ingest-source, call kb_status, then kb_list_raw(changed_only=true) with an explicit limit large enough for the batch, prioritize missing_source_note, honor directory/topic filters, compile in small batches, then kb_rebuild_indexes

clean up these pages, fix placeholder titles, 只修 AI 相关内容, 做一次维护清理:

use maintain-wiki, start with kb_lint, then use kb_search plus kb_read_notes to target placeholder titles, placeholder summaries, placeholder open questions, placeholder related links, stale navigation, and missing high-value derived pages

add concept pages, add entity pages, add synthesis pages, what pages are missing?, 补 concept/entity/synthesis:

use map-gaps, search first, read the evidence, then call kb_map_gaps; use kb_promote_gap when a candidate can land directly, otherwise refine it through kb_prepare_derived_note plus kb_upsert_derived_note

repair source ids, repair manifest drift, 修 source id / manifest 漂移:

run kb_repair_source_ids as a dry run first, explain the plan, apply only if the repair set looks correct, then kb_rebuild_indexes

answer this from the wiki and save it back, 问答并沉淀成页面:

use ask-and-file, read the evidence first, then choose output for query-specific archives or concept/entity/synthesis for reusable knowledge

Important:

honor explicit scope like top 5, first 10, only AI-related, only raw/书评 1/, or do not modify yet

prefer small, high-confidence batches over giant rewrites

for concept/entity/synthesis work, never create a derived page without first reading the supporting notes

Chinese Intent Lexicon

Treat the following Chinese phrases as strong routing hints, even when the user does not mention tools or page types explicitly.

看一下, 检查一下, 盘一下, 总览, 扫一眼, 先看看:

default to inspection-first behavior with kb_status plus kb_lint

先别改, 先不要修改, 只看不改, 先给我报告:

do not call write tools; stay read-only unless the user later approves changes

补缺失, 补 source, 补书评, 编译缺的, 继续补 source note:

treat this as ingest-source focused on missing_source_note

整理一下, 清理一下, 修一下占位内容, 只修 AI 相关内容, 把这部分弄干净:

treat this as maintain-wiki with topic filters and placeholder cleanup

补概念页, 补 entity, 补 synthesis, 沉淀成页面, 把这个主题写成 page:

treat this as derived-page work; search first, read evidence, then use kb_map_gaps or direct derived-note creation

修漂移, 修 source id, 修 manifest, 先 dry run:

route to kb_repair_source_ids with a dry run before any apply step

继续推进这份库, 继续往前做, 接着跑一轮, 往前推进一批:

treat this as an incremental maintenance batch, not as an open-ended rewrite

Continuation Defaults

When the user says 继续推进这份库 or an equivalent continuation request without enough scope detail, run a conservative default batch:

1. call kb_status 2. call kb_list_raw with changed_only=true and an explicit limit 3. call kb_lint 4. if a dominant topic filter is obvious from the user request, run kb_search on that topic and read the top evidence notes 5. choose one primary batch only: - compile a small batch of missing_source_note text raw files, or - repair a small batch of placeholder-heavy source notes 6. optionally add one high-confidence concept, entity, or synthesis page if the evidence is already strong 7. call kb_rebuild_indexes 8. report what changed, what still looks weak, and the next best batch

Do not silently expand an underspecified continuation request into a large multi-hour rewrite.

Scenario Presets

Use these presets when a short request clearly matches one of the common recurring workflows.

ai-topic-cleanup

Strong triggers:

整理一下 AI 相关内容

只修 AI 相关

补 AI 概念页

把 AI 这部分做扎实

继续推进 AI 主题

Default topic cluster:

AI

AIGC

ChatGPT

prompt engineering

AGI

AI hype vs real value

Default batch:

1. call kb_lint 2. run topic-focused kb_search 3. read the top evidence notes with kb_read_notes 4. repair a small batch of placeholder-heavy source notes first 5. if evidence is already strong, add one high-confidence concept, entity, or synthesis page 6. call kb_rebuild_indexes

Priority order:

placeholder source titles

placeholder open questions

placeholder related links

missing high-value concept/entity/synthesis pages

book-review-batch

Strong triggers:

补书评

继续编译书评

把书评编进 wiki

书评批处理

补书评 source notes

Default scope:

only raw/书评 1/

text-readable raw files only

prioritize missing_source_note

Default batch:

1. call kb_status 2. call kb_list_raw(changed_only=true) with an explicit limit 3. filter to raw/书评 1/ 4. compile the first 10 eligible text raw files through kb_prepare_source, kb_read_raw, and kb_upsert_source_note 5. call kb_rebuild_indexes

Writing expectations:

human-facing title, never 元数据

real Summary, Key Points, Evidence, Open Questions, and Related Links

grounded only in the raw content actually read

continue-this-library

Strong triggers:

继续推进我的这份库

接着跑一轮

再往前推进一批

今天继续做这个库

Default decision rule:

if missing_source_note backlog is obviously dominant, prefer a small ingest batch

otherwise if placeholder-heavy source notes are clearly dominant, prefer a small cleanup batch

otherwise read evidence and add one high-confidence derived page

Default batch cap:

up to 10 source-note compiles, or

up to 5 placeholder repairs, or

1 derived page

Do not mix all three into one large batch unless the user explicitly asks for a broader sweep.

One-Line Shortcuts

These are valid compact requests. Treat them as sufficient instructions unless the user adds more scope.

用 $llm-knowledge-bases 检查一下我的 wiki，先别改。

用 $llm-knowledge-bases 补书评前 10 个。

用 $llm-knowledge-bases 整理一下 AI 相关内容。

用 $llm-knowledge-bases 补 3 个 concept pages。

用 $llm-knowledge-bases 修一下 source id 漂移，先 dry run。

用 $llm-knowledge-bases 继续推进我的这份库。

Canonical Actions

Treat the following as the four canonical high-level actions for this skill.

ingest-source

Use this when the user wants to ingest, compile, or refresh changed raw material.

Sequence:

1. call kb_status 2. call kb_list_raw with changed_only=true 3. for each changed raw file: - if the raw file is text or structured data: - call kb_prepare_source - call kb_read_raw - compile the raw content into one grounded source note - if the raw file is a PDF or image: - call kb_prepare_source_bundle - call kb_get_raw_asset - if compile_readiness is not ready, create the missing representation trail with: - kb_prepare_representation - kb_upsert_representation - call kb_read_representations - compile the source note from the raw metadata plus the reviewed representations - call kb_upsert_source_note 4. after the batch, call kb_rebuild_indexes 5. report what was compiled, what remains partial, and which raw assets still need representation work

Important:

the actual LLM compile happens between the read/bundle steps and kb_upsert_source_note

kb_read_raw is only for text-readable raw files

PDFs usually become compile-ready once there is native_text, ocr_text, or page_notes

images usually become compile-ready once there is vision_notes

structured data is already text-readable, but metadata or data_profile can still improve later maintenance

one raw file may later justify new concept, entity, or synthesis pages

prefer incremental passes over giant rewrites

ask-and-file

Use this when the user wants a grounded answer and the answer may deserve a durable artifact.

Sequence:

1. call kb_search 2. read only the most relevant notes with kb_read_notes 3. answer only from retrieved notes 4. decide the best write-back target: - use kb_prepare_output + kb_upsert_output for question-specific answer archives - use kb_prepare_derived_note + kb_upsert_derived_note when the answer should become a durable concept, entity, or synthesis page 5. call kb_rebuild_indexes if the wiki changed materially

Important:

search before answering

do not cite notes you did not read

do not answer directly from non-text raw assets when the runtime already expects the grounded source note or stored representation trail

choose output when preserving the exact query matters

choose concept/entity/synthesis when the answer is reusable beyond the original query

maintain-wiki

Use this when the user wants cleanup, organization, or a quality pass.

Sequence:

1. call kb_lint 2. inspect wiki/index.md, wiki/log.md, and the most relevant collection indexes with kb_read_notes 3. identify weak pages, missing derived pages, stale navigation, or grounding gaps 4. if source note ids, manifest entries, source note paths, or stored raw hashes have drifted, call kb_repair_source_ids first as a dry run and only apply it when the plan is correct 5. if the user wants fixes, repair narrowly through the appropriate kb_* write tools 6. call kb_rebuild_indexes

Important:

prefer small targeted improvements

keep source grounding visible

use concept/entity/synthesis pages to absorb recurring structure instead of repeating the same reasoning in outputs

treat kb_lint warnings as signals about wiki health, not only schema correctness

pay special attention when kb_lint surfaces missing_representation, representation_stale, unreviewed_asset_source, stale source coverage, unresolved research gaps, unsupported claims, contradiction candidates, or missing high-value pages

use kb_repair_source_ids for deterministic source-note/manifest repair instead of hand-editing ids, paths, or raw hashes

map-gaps

Use this when the user wants to know what the wiki is missing.

Sequence:

1. call kb_search 2. read the relevant source, output, and derived pages 3. call kb_map_gaps 4. identify: - repeated ideas that deserve a concept page - repeated named items that deserve an entity page - cross-source themes that deserve a synthesis page 5. if the user wants the page landed immediately, call kb_promote_gap with the candidate note_id 6. otherwise propose the best next pages in priority order

Important:

prefer candidates with stronger source_refs coverage

use kb_promote_gap when a current candidate should be landed as-is into the wiki

use the returned draft template as the starting point when you want to refine the page before calling kb_upsert_derived_note

treat the returned suggested opening and evidence summary as scaffolding, not final prose

Writing Rules

Source Notes

Required frontmatter fields:

id

type: source

title

raw_path

raw_hash

source_kind

tags

created_at

updated_at

status

Strongly recommended frontmatter fields:

raw_kind

mime_type

asset_paths

Required headings:

# Summary

# Key Points

# Evidence

# Open Questions

# Related Links

Multimodal guidance:

for PDF and image source notes, asset_paths should include the primary reviewed raw asset

add # Visual Notes when the note depends on multimodal review details that are not already obvious from the stored representation files

do not claim a non-text asset was reviewed if neither the representation trail nor the visible review notes support that claim

Output Notes

Required frontmatter fields:

id

type: output

title

query

source_refs

created_at

updated_at

Required headings:

# Answer

# Sources Used

# Follow-up Questions

Derived Notes

Derived pages are for durable wiki structure, not ephemeral chat residue.

Supported kinds:

concept

entity

synthesis

Shared frontmatter fields:

id

type

title

aliases

source_refs

tags

created_at

updated_at

status

Required headings by kind:

concept: # Summary, # Definition, # Key Points, # Evidence, # Open Questions, # Related Notes

entity: # Summary, # Who or What, # Key Facts, # Evidence, # Open Questions, # Related Notes

synthesis: # Summary, # Thesis, # Supporting Evidence, # Tensions, # Open Questions, # Related Notes

Guidance:

concept pages capture reusable ideas or frames

entity pages capture named things that recur

synthesis pages capture higher-level cross-source conclusions that should survive beyond any one query

synthesis pages capture multi-source analysis, tradeoffs, or contested views

keep source_refs aligned with real source notes

Safety Boundaries

Never modify files under raw/.

Never use generic file-writing tools to modify wiki/ directly.

Never invent note IDs, output IDs, or paths.

Never claim a write succeeded unless the runtime confirms it.

Never bypass the runtime after a tool failure.

Failure Handling

If a runtime tool fails:

explain the failure plainly

keep the user context intact

propose the next executable step

do not work around the error by writing directly to the Vault

Language Policy

Write outward-facing artifacts in English by default.

Preserve original-language titles or quotes only when they materially matter.

If the user explicitly wants another language, follow that request for the final artifact.

Finish Standard

When you finish a task with this skill, report:

what was ingested, answered, maintained, or mapped

which kb_* tools were used

which files were created or updated

any unresolved ambiguity or weak evidence

the best next one to three follow-up steps