ontology-docs 0.3.0

A standalone, searchable Ontology documentation and agent-skills demo.
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ONTOLOGY 
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Wg�3�5I��)X�bjS����(RLINK@%S�S�{˯G��[G��^p��p2��m�����a�{"format_version":3,"name":"ontology-docs","document_count":6,"chunk_count":43,"embedding":{"kind":"feature-hash","model":"feature-hash-v1","dimensions":384},"image_count":1,"skill_count":3,"image_embedding":null}(�/�X��(�BPG��033333+�i�1F��,�R4�D��mI$3R�C���c�����fd�K��D3�q~���!d�	�}L��9-�s�ge�����Gy�‚�^����M"I�G�L����m�O��������Ǽ��I��s��|?�x*/�Kk]nC^B t6^�E�Ja�IJr-�k���Fy��g�N����T�y�/��S�M���ϫ��윅b�[�=F�:��V�ǘϫ2?yZ%
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(�#����Y��0Lj�0f��1��p�=jLcW[{"id":"historical-research","path":"research/competition-history.md","name":"Historical competitor investigation","media_type":"text/markdown","source":"ontology://docs/research/competition-history.md","document_id":"competition","offset":0,"bytes":26059},{"id":"sample-bytes","path":"attachments/sample.bin","name":"Original binary demonstration","media_type":"application/octet-stream","source":"ontology://docs/attachments/sample.bin","document_id":"usage","offset":26059,"bytes":7},{"id":"nested-example","path":"packages/example.ontology","name":"Nested example package","media_type":"application/vnd.ontology","source":"ontology://docs/packages/example.ontology","document_id":"usage","offset":26066,"bytes":6769}]# Ontology competitor research

Accessed **2026-10-16 UTC**. Scope: a native executable containing a document corpus, search indexes, vectors and an agent interface, optionally unlocked by a password. Lens: distribution and operational simplicity. Assumes immutable snapshots and a bounded corpus that fits memory. Breaks when the requirement becomes Wikipedia-scale random access, mutable shared storage or a hosted application.

**Recommendation:** ship an executable knowledge package. The useful combination is Kiwix's offline archive, llamafile's data-plus-runtime distribution and a small search/read interface. Password protection should encrypt the entire package with the established age file format; executable code remains executable, and no key is embedded. Keep `.ontology` as a library/intermediate format. Make one `build INPUT... --output EXECUTABLE` command the product path.

This report inspected current official documentation and project source. SWOT entries are design judgments drawn from the linked mechanisms, not vendor claims. No products were benchmarked. “Gap” means the inspected evidence does not establish the claim; it does not mean a feature cannot exist.

## Comparison

| Approach | Relationship | What actually ships / runs | Useful mechanism | Password protection established by evidence |
|---|---|---|---|---|
| [Kiwix / libzim](https://github.com/kiwix/kiwix-tools#readme) | Closest archive substitute | ZIM archives plus a reader/search tool or HTTP daemon | Offline content, metadata and full-text search | Gap: inspected archive API/header do not establish native password-encrypted ZIM support |
| [OpenZIM MCP](https://github.com/cameronrye/openzim-mcp#readme) | Direct agent-facing competitor | Python/uv or Docker runtime plus separate ZIM archives | Offline MCP search/read/sections | HTTP bearer authentication documented; portable encrypted archives not established |
| [llamafile](https://github.com/mozilla-ai/llamafile#readme) | Strongest distribution inspiration | Executable containing model weights and inference runtime | Bind data to a compatible reader/runtime | Gap: a password-encrypted embedded model mode was not established |
| [Datasette](https://docs.datasette.io/en/stable/cli-reference.html), [FTS5](https://www.sqlite.org/fts5.html), [sqlite-vec](https://github.com/asg017/sqlite-vec#readme) | Closest simple storage alternative | SQLite data plus runtime; Datasette serves HTTP; a Rust program can embed SQLite | Existing lexical/vector engines in one database | Datasette permissions concern requests; SQLite SEE explicitly encrypts database content, under separate licensing |
| [Pagefind](https://pagefind.app/docs/running-pagefind/) | Immutable search-build inspiration | Static index and browser dependencies in a directory | Build once; query without a search service | Gap: native password-encrypted search bundle not established |
| [Dash / Zeal docsets](https://kapeli.com/docsets#dashDocset) | Documentation package substitute | HTML directory, SQLite symbol index, installed browser | Small manifest/index and direct anchors | Gap: password-encrypted docset support not established |
| [LanceDB / Lance](https://docs.lancedb.com/lance) | Retrieval-library alternative | Embedded SDK plus table files/manifests/indexes | Text/vector hybrid retrieval and model metadata | Enterprise encrypts object store and cache at rest; portable password-encrypted local snapshot remains a gap |
| [AnythingLLM](https://docs.anythingllm.com/installation-docker/overview) | Adjacent full application | Desktop app or Docker service with persistent storage | Ingestion-to-retrieval workflow | Docker password gate documented; corpus/index/vector encryption tied to that password not established |

## SWOT and what to take

### Kiwix / openZIM

**Strength:** Kiwix already provides offline full-text search and serving of packaged archives. **Weakness for this mission:** archive and runtime are distinct distribution units. **Opportunity for Ontology:** carry the reader and corpus together and expose agent-sized responses. **Threat:** existing ZIM collections and their readers already solve much of the offline-wiki need. The tools explicitly distinguish `kiwix-search`, `kiwix-serve` and archive management. [Kiwix tools README](https://github.com/kiwix/kiwix-tools#readme)

Borrow compressed content clusters, stable entry identity and archive metadata. Reject adopting the complete C++ stack solely to obtain these ideas: libzim uses ICU, compression libraries and optional Xapian; its search API disappears when compiled without Xapian. Its GPLv2-or-later license also changes the integration choice for an MIT crate. This is a dependency/license decision, not a performance conclusion. [libzim dependencies, search build option and license](https://github.com/openzim/libzim#readme)

Encryption check went beyond the landing page: inspected the actual [file header writer](https://github.com/openzim/libzim/blob/e4de4b7a26366a19290ee2b0300b7ea878d9cd92/src/fileheader.cpp#L54) and [archive-open API](https://github.com/openzim/libzim/blob/e4de4b7a26366a19290ee2b0300b7ea878d9cd92/include/zim/archive.h#L121). They establish header fields and path/file-descriptor opening, not a password mode. **Gap: native encrypted ZIM support — no affirmative evidence in these surfaces.** The wiki format page was blocked by its anti-bot gateway; no claim relies on its unseen contents.

### OpenZIM MCP

**Strength:** this is already an offline archive-to-agent bridge, with search, article/section retrieval, metadata and stdio MCP. **Weakness:** documented installation still needs uv/Python or Docker and a ZIM directory. **Opportunity:** Ontology removes that assembly and makes package identity/provenance part of each result. **Threat:** “MCP over an offline wiki” alone is already available. [Maintainer README and installation](https://github.com/cameronrye/openzim-mcp#readme)

Borrow bounded retrieval, pagination and section-level reads. Reject its broader surface as a default: it offers one-tool and eight-tool modes, HTTP, subscriptions, prompts and a separately built link-graph SQLite sidecar. Ontology needs deterministic `search`, `read` and package information before graph browsing or server-side natural-language dispatch. Its documented HTTP bearer token controls service access; it is not evidence that copied archives are encrypted. [OpenZIM MCP modes and deployment](https://github.com/cameronrye/openzim-mcp#readme)

### llamafile

**Strength:** the executable carries assets and the runtime that knows how to use them. **Weakness:** its cross-platform mechanism is a specialized toolchain/runtime integration; it is not ordinary Rust cross-compilation. **Opportunity:** adopt the distribution contract with target-specific Rust binaries. **Threat:** single-file AI distribution is already a credible existing pattern, so Ontology's retrieval/citation experience must matter. Mozilla documents concatenated architecture builds and embedded PKZIP weights mapped into memory. [How llamafile works](https://docs.mozilla.ai/llamafile)

Borrow version binding and a file that does useful work immediately. Reject bundling a generation model or adopting Cosmopolitan in the first retrieval package. Current upstream documentation explicitly calls out Windows' 4 GB executable limit and distinguishes the 0.10 build generation from older releases. Do not advertise one binary that runs everywhere merely because llamafile does. [Current upstream README](https://github.com/mozilla-ai/llamafile#readme)

Encryption review included its [security document](https://github.com/mozilla-ai/llamafile/blob/a3ccf098ed9c7c77687f5430b0143156d3bcc45a/docs/security.md), which addresses execution/server behavior. **Gap: password-encrypted embedded assets — not established.**

### Datasette / SQLite FTS5 / sqlite-vec

**Strength:** the data file is inspectable using general-purpose tools; FTS5 already has BM25, phrase/prefix queries, snippets and highlighting. **Weakness:** those tools do not themselves produce Ontology's executable/agent contract. **Opportunity:** SQLite could replace substantial custom indexing/storage code if that code becomes the maintenance burden. **Threat:** a small Rust wrapper around SQLite can reproduce a large share of the proposed capability. [FTS5 API and ranking](https://www.sqlite.org/fts5.html)

Datasette supplies exploration and HTTP APIs, with Python installation or its browser/WASM alternative. Its `package` command creates Docker containers, not a native executable carrying the data. Borrow inspectability; reject requiring Datasette itself. [Getting started](https://docs.datasette.io/en/stable/getting_started.html), [CLI packaging contract](https://docs.datasette.io/en/stable/cli-reference.html)

sqlite-vec is pure C without additional dependencies, supports float/int8/binary vectors and has Rust integration. Its README explicitly warns that it is pre-v1 and can break compatibility. A frozen Ontology package must pin its reader/index compatibility rather than promise indefinite compatibility with arbitrary extension upgrades. [sqlite-vec README](https://github.com/asg017/sqlite-vec#readme), [official Rust integration](https://alexgarcia.xyz/sqlite-vec/rust.html)

An embedded database need not be extracted: SQLite's `deserialize` API opens serialized bytes as an in-memory database and supports a read-only mode. That makes SQLite technically compatible with the single-executable/decrypt-in-memory design. This is an architectural option, not a measured recommendation to rewrite working code. [SQLite deserialize](https://www.sqlite.org/c3ref/deserialize.html)

Do not conflate login and encryption: Datasette's actors/permissions gate requests. SQLite SEE separately documents encryption of all database content including metadata and separate commercial licensing. Neither establishes that stock Datasette encrypts its databases. [Datasette authentication](https://docs.datasette.io/en/stable/authentication.html), [SQLite SEE](https://www.sqlite.org/see/doc/trunk/www/readme.wiki)

### Pagefind

**Strength:** generated static search avoids a database service. **Weakness:** output is a directory of browser/search dependencies, not an executable knowledge package. **Opportunity:** use its build/query split and index only meaningful content. **Threat:** static documentation publishers may already get sufficient search without Ontology. The documented build writes a `pagefind` directory alongside the site. [Build/output contract](https://pagefind.app/docs/running-pagefind/)

Borrow content selectors that skip navigation/footer noise and keep section metadata. Borrow loading result bodies only after selecting hits. Reject building a browser UI, remote-fetch layout or plugin system to imitate it. [Content selection](https://pagefind.app/docs/indexing/), [result API](https://pagefind.app/docs/api/)

Its output source writes separate metadata, fragment and word-index artifacts. This establishes a static packaging mechanism, not encryption. **Gap: native password-encrypted index support — not established by the inspected output implementation.** [Pinned output writer](https://github.com/Pagefind/pagefind/blob/1b02d99a267f6a24792b9fca39b897081d108044/pagefind/src/output/mod.rs#L261)

### Dash / Zeal docsets

**Strength:** the format is deliberately small: HTML plus a SQLite index of entry name, type and target path, including anchors. **Weakness:** the reader is separately installed; this contract does not carry vectors or an agent runtime. **Opportunity:** reuse the idea of direct source/section addresses with a tiny manifest. **Threat:** for symbol lookup, a richer semantic stack may add no value. [Dash generation contract](https://kapeli.com/docsets#dashDocset)

Zeal consumes the same format and supports scoped searches from its CLI, but is a Qt/WebEngine browser. Borrow predictable addresses and scoped lookup; reject desktop rendering and app-install dependency. **Gap: password-encrypted docsets — neither the generation contract nor the inspected reader documentation establishes it.** [Zeal README](https://github.com/zealdocs/zeal#readme)

### LanceDB / Lance

**Strength:** local Rust/Python/TypeScript retrieval already includes text, vectors, metadata and hybrid ranking. **Weakness for this mission:** Lance tables comprise data, version manifests and index files, with lifecycle machinery for mutation. **Opportunity:** borrow normalized result IDs and explicit embedding metadata. **Threat:** teams already on LanceDB need packaging, not a competing database. [LanceDB repository](https://github.com/lancedb/lancedb#readme), [pinned table layout](https://github.com/lancedb/lance/blob/42e593fb4ad0e1ccc22c1fa5e3c30bfe92a5384d/docs/src/format/table/layout.md)

Borrow reciprocal rank fusion, already its default hybrid reranker. More importantly, preserve the distinction between stored vectors and a query encoder: its embedding layer can reconstruct a configured function, or callers supply query vectors explicitly. Stored vectors alone do not make semantic text queries self-contained. Reject a provider registry, branches, cloud storage and compaction until an actual workload requires them. [Hybrid search](https://docs.lancedb.com/search/hybrid-search), [embedding behavior](https://docs.lancedb.com/embedding), [versioning/compaction](https://docs.lancedb.com/lance)

Encryption is affirmative for **Enterprise object store and cache**, not inferred from “local” or “private.” **Gap: portable password-encrypted OSS local table — not established.** [Enterprise security](https://docs.lancedb.com/enterprise/security)

### AnythingLLM

**Strength:** ingestion, embedding and document-backed interaction are integrated. **Weakness for this mission:** documented storage spans parsed documents, vector database, embedding cache, models, SQLite and other directories. **Opportunity:** make ingest failures and model requirements obvious, while distributing one immutable result. **Threat:** users wanting a complete chat/agent application may prefer an existing UI. [Documented desktop storage](https://docs.anythingllm.com/installation-desktop/storage)

Borrow an understandable document-to-retrieval workflow. Reject workspaces, chat history, user management and agent execution. Its official platform table says password protection exists in Docker and not Desktop. That is an application access distinction. [Platform comparison](https://docs.anythingllm.com/installation-docker/overview)

Verified implementation distinction: single-user request validation checks authorization/JWT content against `AUTH_TOKEN`; an encryption helper protects arbitrary strings using separately managed key material. This does not establish that parsed documents, LanceDB indexes and embeddings are encrypted under the login password. **Gap: whole-corpus password encryption — not established.** [Request validation source](https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/middleware/validatedRequest.js), [encryption helper](https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/EncryptionManager/index.js)

Its deletion model also distinguishes removing vectors from a workspace from deleting parsed text and cached embeddings. Ontology should avoid these independent copies in its shipped artifact. [Privacy/data handling](https://docs.anythingllm.com/features/privacy-and-data-handling)

## Smallest coherent Ontology

These are recommendations, not claims that the current implementation already meets them.

1. **One product operation:** `ontology build INPUT... --output knowledge [--password]`. Import → normalize/chunk → build search structures → optionally encrypt → compile native wrapper. The final executable is what is handed to an agent. Keep `pack`/`bundle` as composable library/developer operations; do not require the user to understand them to ship a dataset.
2. **One read-only runtime:** default `--help`, `inspect`, `search`, `read`, `serve`. MCP should expose package information, search and read; retain extra CLI inspection commands only where useful. Return stable IDs, short excerpts and source/offset citations; fetch complete text deliberately. No generation model, shell executor, HTTP daemon or account system.
3. **One immutable logical package:** corpus/provenance, lexical index, optional vector matrix and embedding identity. Keep the custom container thin and frozen. Do not invent a database, generic schema registry or independent update log. SQLite FTS5 is the strongest alternative if custom posting/query code becomes costly; do not ship both storage engines now.
4. **Full text always works offline.** Flat exact vector scan is sufficient until corpus-size measurements disprove it. A feature hash is a lexical baseline, not learned semantics. External vectors require a matching external query vector. If standalone natural-language semantic search is promised, a compatible query encoder and its assets must also be embedded. Pick one encoder only when that promise becomes an acceptance criterion.
5. **One target per artifact initially.** Target-specific native Rust builds are a coherent “one binary” promise. Universal executables, GPU support, ANN, OCR, crawling, incremental updates and registries each need evidence of demand before entering the runtime.

The current local design already chooses a small sectioned format and exact vector scans. The research does not justify replacing this working first implementation with a lakehouse. It does challenge treating custom BM25 internals or the file format as the moat: SQLite already provides the important search primitives. The differentiator is the artifact's distribution and agent contract.

## Password protection: use an age envelope

**Choose the Rust age library over a new Ontology cryptographic format.** age defines passphrase-based scrypt key wrapping with a fresh salt, a random file key, an authenticated header and chunked ChaCha20-Poly1305 payload encryption. Its final-chunk marker detects truncation. All inner `.ontology` bytes become encrypted payload, including manifest, directory, checksums, corpus, postings and vectors. Ontology adds no plaintext semantic metadata outside that envelope. [age specification](https://c2sp.org/age@v1.1.0)

The Rust library has passphrase `Encryptor`/`Decryptor` APIs and memory/stream I/O. Embedding the resulting ciphertext does not require the age executable on the recipient machine. Its current docs serve version 0.12.1 and explicitly call pre-1.0 releases beta; pin and test the chosen crate rather than claiming its implementation is audited or stable. The established file format and implementation maturity are separate claims. [Rust age API](https://docs.rs/age/latest/age/)

An Argon2id + RustCrypto XChaCha20-Poly1305 implementation is viable at the primitive level: the crates expose key derivation and AEAD. It makes Ontology responsible for salt/nonce management, KDF policy, versioning, header associated data, length handling, truncation and future interoperability. That is extra protocol ownership without a current product benefit. Use it only if an actual requirement rules age out. Never implement either cryptographic primitive. [Argon2 key derivation](https://docs.rs/argon2/latest/argon2/), [RustCrypto AEAD](https://docs.rs/chacha20poly1305/latest/chacha20poly1305/)

Recommended behavior:

- **Encrypt before bundling.** Protected builds must never place a plaintext package, extracted document, vector export, password or key in generated Rust source, temporary projects, compiler cache, diagnostics or binary resources. Embed the exact encrypted bytes unchanged. A build can hold plaintext in RAM, then write only ciphertext to its temporary project. Existing input documents remain the caller's inputs; the shipped artifact adds no plaintext copy.
- **Unlock once per process.** Accept a hidden TTY prompt or a bounded password input from a pipe/file/descriptor. Never accept plaintext password values in command arguments. A file argument names a file; its contents are not copied into argv. Specify newline handling and maximum length; reject empty input. Confirm interactive passwords when building.
- **Reserve MCP stdin for JSON-RPC.** For `serve`, use a TTY or a separate password file/descriptor. Do not consume a password line from the same stdio stream as MCP. Missing credentials in a noninteractive process fail immediately with an actionable stderr error, rather than waiting forever or writing prompts to stdout.
- **Authenticate before use.** Decrypt to a bounded, zeroizing in-memory buffer and read successfully through authenticated EOF before parsing or exposing any inner content. Streaming AEAD can authenticate earlier chunks before a later failure; Ontology should release no partial query results or metadata. Then apply the ordinary inner container validation and decompression limits.
- **Bound work before authentication.** The password library must parse some attacker-controlled envelope data first. Configure a maximum scrypt work factor, header/input length and decrypted size. Do not blindly inherit a workstation-dependent large KDF limit. The age identity API provides `set_max_work_factor` and explicitly documents CPU/memory denial-of-service from large values. [Identity work limit](https://docs.rs/age/latest/age/scrypt/struct.Identity.html#method.set_max_work_factor)
- **Fail closed.** A wrong password or failed authentication produces a nonzero result and a short “cannot unlock: wrong password or damaged package” error. No fallback to an unencrypted interpretation, no empty-success package, no result text. Keep unsupported version and excessive-resource errors separately actionable without disclosing inner metadata.
- **Keep format metadata authenticated.** age authenticates its own header; the inner Ontology header/directory are covered by the encrypted payload. Do not add an unauthenticated outer name, embedding dimension or codec selector that changes interpretation. If an outer wrapper is later introduced, put security-relevant copies inside the authenticated payload and check equality, or choose a format with explicit associated-data support. No custom MAC glued around age.

### Exact protection boundary

The password protects the packaged **data at rest**: source text, titles/provenance, term dictionary/postings and vectors. Encrypting just documents leaves searchable information behind. The OS must still load executable code; code and generic CLI behavior are not concealed. Artifact size, target platform and the fact that it is encrypted remain visible.

An authorized running reader can recover plaintext. A supplied password cannot enforce copy prevention, per-document permissions, revocation of an already copied package or a query allowance. A modified reader can export everything. Password-based authentication also does not authenticate the publisher: anyone knowing the shared password can create replacement ciphertext. The host, swap/core dumps and an agent's own output/logging are outside the encrypted artifact; zeroizing buffers reduces residual copies but is not a claim of protection from the running host.

Encrypted output should be randomized, even when inputs are identical. Preserve deterministic plaintext package generation as a separate property. Never reuse salts/nonces to make encrypted executables reproducible.

## Decisions and evidence still needed

- **Decide the semantic promise:** external query vectors keep runtime small; natural-language learned semantic queries with no external dependency require an embedded encoder. Stored embeddings alone do not settle this.
- **Measure the corpus envelope:** current design's in-memory decode and exact scans need startup/RSS/package-size measurements against representative docs and a large wiki subset. No evidence here establishes acceptable latency or maximum useful corpus size.
- **Verify protected-build hygiene:** search a built binary and generated build/cache artifacts for unique corpus canaries; verify correct password, wrong password, header/ciphertext tampering, truncation, excessive scrypt work, moved executable and stdio MCP. Show no plaintext temporary output. This is required implementation evidence, not an inference from using a crypto crate.
- **Version/policy gap:** choose and lock the Rust age version and explicit decryption resource ceiling using target-machine measurements. The report verifies APIs and specification, not the security of an unimplemented integration.
- **Compatibility gap:** no platform execution tests, ingestion-quality comparison or retrieval benchmark was performed in this research task. No universal-binary or speed claims follow.

## Source verification record

All 38 unique linked URLs above were opened and inspected on 2026-10-16, then fetched again successfully (HTTP 200; GitHub source pages checked through their equivalent raw source URLs). Primary sources only: maintainer documentation, specifications, public API references and source. Mutable repository pages are dated observations; selected implementation references are pinned to the observed commit. AnythingLLM's default source branch is `master`; its linked code is a dated observation rather than a pinned snapshot.

The official ZIM wiki format URL returned an anti-bot access denial; libzim source was used instead. Several docs URLs with trailing slash or `.md` returned reader errors; their successful canonical HTML pages or directly fetched official Markdown were inspected. These failed fetches were not treated as negative feature evidence.

Local context read before research: `docs/design.md`, `README.md`, `git status --short`, `git log -3 --oneline`. The worktree already contained the uncommitted 0.2.0 implementation. This task writes only this report; no product files, commits, publishing or agent coordination through ICQ.
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See [format](format), [agents](agents) and [overview](overview) for\ncontainer details, skills, citations and integrations. There is no universal binary,\nOCR engine, crawler or bundled diffusion model. 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Start with [HTTP/custom dashboards](ontology-document:http)\nor [the format guide](ontology-document:format) for schemas, nested unlocks and scoring limits.\n\n## Rust library\n\n```rust\nuse ontology::{BuildOptions, Package,","start_char":4797,"end_char":4845,"components":[0.49494392,0.71948963,0.6,0.3868528]},"overridden":false},{"id":"9b88b7be477c55816a3b8d6c2b463b2a0b483ceb147cdefff8ba7c278ce114e3","source":"asset:nested-example","target":"nested-package:nested-example","relation":"encapsulates","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"9cd3422417fdd9ad4104e80233095fd735e20906fb4c815e5ea4bc49b4e5d427","source":"chunk:ff09fe1884113397dbd5ead7734a82f874c92dd4434e5f7603842071785f1b04","target":"text-embedding:ff09fe1884113397dbd5ead7734a82f874c92dd4434e5f7603842071785f1b04","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"9f12d8f0f7d025c30ec2f88c3ae86ecb196c5721803a00b92bf310eb267b0e18","source":"document:overview","target":"chunk:dfeed1100f1dc6b56ac88b8927722a4a1c01a58ea4c4d94ecea1b1319344e99f","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"a0006b4f74a63124cc93afdb9344bffc992367ba340436f00f207877c0bbd7f5","source":"chunk:cf22b776cbeed365aa51e4e340517e99a4afb02b7e1e031725656e2933e245c2","target":"text-embedding:cf22b776cbeed365aa51e4e340517e99a4afb02b7e1e031725656e2933e245c2","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"a03ebc84e9082f8a479ca71b03a17637f3945dfd6fc6d43d6b10947e7c4b52ec","source":"document:competition","target":"chunk:4741a6580692c6ec2aebb5fba17f4f3e87f0097b0c90d0d1ffa70f04fbf547fc","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"a4d43425e97ba2f8c90a24d394436c50616f6b760d4280cfe904f024e0c7bba4","source":"skill:ontology-skill-creator","target":"resource:ontology-skill-creator/SKILL.md","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"a8eb9bdcbd46043b96d9ff4a4bbc93dc8a2ca21521c811bd3922206be588e744","source":"document:format","target":"chunk:c6c079fc6540bcc19f1c88c42b50be7e0cfe89dc79b8c629458207370fe0fe90","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"a9e6cc433d585e7c49e1d9603dbb8384fe9b6d44c49b5c4dd729101eeedac947","source":"chunk:aac31cf4d1ed74c13907bec6d2b0366e200826216cf4c420ee3e4bb1ab5ed9e6","target":"text-embedding:aac31cf4d1ed74c13907bec6d2b0366e200826216cf4c420ee3e4bb1ab5ed9e6","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"ab9e695a76ec7ce6aa00b6d46f734f07b1f1cb3f4e688cc290e81de7d84b44ae","source":"document:usage","target":"asset:sample-bytes","relation":"references","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":{"href":"ontology-asset:sample-bytes","anchor":"the sample attachment","context":"d explicit generic assets retain their original bytes.\n\nRun `objects`, `object document:usage`, `graph`, `links`, `assets`, `embeddings`\nor `packages`. Inspect [the sample attachment](ontology-asset:sample-bytes) and\n[the nested package](ontology-asset:nested-example). `open nested-example` returns\nits scope; `list --package-path nested-example` explores that child. Par","start_char":2423,"end_char":2475,"components":null},"overridden":false},{"id":"aef71d8fb1829586b100eaa393007b4a317231ee9f033395ab13ca0ee409a392","source":"document:format","target":"chunk:52dbae52c5d263ae25abb0cc6e5d8bebc4ece19f17f54fc61e25ce99d0ca4a1d","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"afc69116118ee235ae2fc27c05b7e04f8f11a87a83bec8756a756672e8c29ba6","source":"chunk:7f0865b770e078ca3a59116838e5236200bd443581cb5dbb39f7a5ef34f27a1d","target":"text-embedding:7f0865b770e078ca3a59116838e5236200bd443581cb5dbb39f7a5ef34f27a1d","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"b074a786493806a925173cba890b5f7a67ca90042dbbc3aef47790aed0506411","source":"document:usage","target":"asset:nested-example","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"b3606312480c2d9c20a5ff4cb6413975a7b15649b408a4369777f8151b082283","source":"chunk:e4ffa70bac5a3c61aee0b30cd5706e759868231c86bdd80b476c0b78fc45f7c5","target":"text-embedding:e4ffa70bac5a3c61aee0b30cd5706e759868231c86bdd80b476c0b78fc45f7c5","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"b800975d8230cf8c26139ae5fab9195510ac2ff5778aa479eafb853e51dd58e5","source":"package:root","target":"document:competition","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"b803ce0c1091254b0d5ad1a20b49040ab04c1decc51ce719f10aeece9a4e5c17","source":"document:format","target":"chunk:ff09fe1884113397dbd5ead7734a82f874c92dd4434e5f7603842071785f1b04","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"b9194f56a9be9ceff0099d158431578c343be526c7a2df8963008e0dd20776b8","source":"document:usage","target":"document:http","relation":"references","weight":0.61823016,"method":"lexical-proxy-v1","model":"feature-hash-v1","revision":null,"evidence":{"href":"http","anchor":"HTTP API","context":"tural contains edges\nare separately labelled. Explicit finite weights retain author/model/revision provenance.\n\nRun `server --port 0` to open the dashboard and [HTTP API](http). It uses the same\nread-only operations as CLI and MCP. No attached executable or skill resource runs.\n","start_char":3448,"end_char":3464,"components":[0.67453057,0.7695249,0.4,0.33333334]},"overridden":false},{"id":"ba3be2f4c246067b8848d7f284f10705349ad4a7ecf2b9f6ee2f953c91f33a43","source":"document:format","target":"chunk:aaf568b6ddddd2ed7c4e89dabe28a42aad89ff2cde1830a1e2cf98f0d0a6f531","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"bc871d128995abe23652f31fac32c93399b1824a9945b8dcde45cfaf3b42e814","source":"document:overview","target":"chunk:b1a2aee4f602af28ea08300bae4935e1a41bb7f4bbd191b79f6ed4407bea26dd","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"bd69dfca43b86810974cce3d2a2399d9883f36829a64517a5fd61b1b4d308469","source":"document:format","target":"chunk:200ad52a5110154dfaa27bcddf0feb6f99872d67c0d91b5b5b07da4acc7114e3","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"be35496242550d79ab209eb14a8c1b40ebf1b26537f07cb40e632458df9567c6","source":"document:format","target":"chunk:d5bd15c55ac121df700777310832199738df42f6efa4ce720fbfacf00d49088b","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"bf5d9ebe049b1b8b049a09c1ff076dee7dba6fe2697c9762121bd26d8c2ed6d8","source":"document:format","target":"chunk:423287668eb439991f6e94e19e5a12eca6f79b77e29447e17c3a7b3cf10f1ce3","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"c2ed6da661c84eb0b6a7220bf4182e4c000d1b934bf8a9c230459f89760f6eec","source":"document:http","target":"chunk:a95823d289e779aee3af3ac269b72d904047ae1fd421a21a0179b4826f84dcab","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"ce27a29b7f190e46e3a97190507158c64cf5b2e135d9322fc74064ba02c398b3","source":"document:format","target":"chunk:ea98f4d0812ed27fcc0b881b90aeb650131d50c61d64b480603b0f15357db049","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"d3c5cfa526490e7a86ddb203307d4d7f0ad000128c99ebdcb8fc4e13a19d3ecd","source":"chunk:05d1d1a5b45edf0b395b7fcdb649cbbb84ae68aecc29f39e20935edbfb057b42","target":"text-embedding:05d1d1a5b45edf0b395b7fcdb649cbbb84ae68aecc29f39e20935edbfb057b42","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"d3d9043c1ea28f500468d67381ccddbd3917d855c796fa8cc4ca54e6ecdc00c5","source":"document:agents","target":"reference:f36ae4007ec3a8cffaebbd586523686380b188fc4aa93d3c708af94e25420357","relation":"references","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":{"href":"https://agentskills.io/specification","anchor":"Agent Skills specification","context":"/code-fence rules. It additionally requires nonempty\nname/description, exact folder-name matching, safe bounded resources and permits\n`compatibility` under the [Agent Skills specification](https://agentskills.io/specification).\n\nThis is a documented compatible subset, not byte-identical PyYAML behavior:\nserde_yaml uses YAML 1.2 scalar rules (`yes`/`on` and bare dates are strings), na","start_char":2036,"end_char":2102,"components":null},"overridden":false},{"id":"d70dca033e969925c951c0e9a2a8e72e75b3402828c21e4b99e3bc12ab0c6d35","source":"chunk:73c67574b76ae0eb278393a094942d089a14652849f863c3cdc830bb49452bbd","target":"text-embedding:73c67574b76ae0eb278393a094942d089a14652849f863c3cdc830bb49452bbd","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"dcdd08c9066904fa514da065a2ad3521f53a4359b8a7878131825826f7b7bf23","source":"chunk:11f60b0e9e18f5f6e7c229413f9a49bd48ccb2d1678002e5e271da568daac694","target":"text-embedding:11f60b0e9e18f5f6e7c229413f9a49bd48ccb2d1678002e5e271da568daac694","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"de6eca888232088171b54b1719f3f66fbb12dbab51c475d95ec98615012b33f8","source":"chunk:4741a6580692c6ec2aebb5fba17f4f3e87f0097b0c90d0d1ffa70f04fbf547fc","target":"text-embedding:4741a6580692c6ec2aebb5fba17f4f3e87f0097b0c90d0d1ffa70f04fbf547fc","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"df29ca2e75451bf297339d2b3488060b0c73062e586719d1feca53a303b5036a","source":"document:usage","target":"document:format","relation":"references","weight":0.59966284,"method":"lexical-proxy-v1","model":"feature-hash-v1","revision":null,"evidence":{"href":"format","anchor":"format","context":"atching model/query coordinates.\n\n## Competition and design\n\nRead [competition](competition) to compare Kiwix, llamafile, Datasette and other\nalternatives. See [format](format), [agents](agents) and [overview](overview) for\ncontainer details, skills, citations and integrations. There is no universal binary,\nOCR engine, crawler or bund","start_char":1546,"end_char":1562,"components":[0.67628527,0.78436255,0.25,0.33333334]},"overridden":false},{"id":"e1d39f4db4bdfcfa8de0b95e237ee20051c24c218e70a18b87c3d5e357949786","source":"document:agents","target":"chunk:c47ed72bad3a05e2bb79870f97eec8691abd11caebd41869d5269d466a5a8509","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"e1edff354db8372c3ca943cfa0e0a2738080755a088bc7a4883d7f262967aab3","source":"chunk:0380e0661cf9925f616f327e82242e8c61423fc02879ffb90bcea02568d1e4df","target":"text-embedding:0380e0661cf9925f616f327e82242e8c61423fc02879ffb90bcea02568d1e4df","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"e2a6e01bb3c516e5fe32b0c6742a32f941a9a0f0eb9aad1ba4dcdf565b0e0aa1","source":"document:competition","target":"asset:historical-research","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"e483c0d9693a6eec5034c497c48c2db367e670c7ce48ba2a777d8225e8ed62cd","source":"document:overview","target":"image:original-atlas","relation":"references","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":{"href":"ontology-image:original-atlas","anchor":"Original gold and teal dataset grid","context":"his release.\n\nDevelopment: `cargo fmt --all --check`, `cargo test --workspace --locked`, and\n`cargo clippy --workspace --all-targets --locked -- -D warnings`.\n\n![Original gold and teal dataset grid](ontology-image:original-atlas)\n","start_char":5881,"end_char":5950,"components":null},"overridden":false},{"id":"e4d5075529c590f12722fa34a03f32988d1b2773d990f3906f1407817a27c9b9","source":"chunk:dc4f5b8df45005703b3ee744575ec89f07e871f4f3b3eb9f4ba361ac874d9b50","target":"text-embedding:dc4f5b8df45005703b3ee744575ec89f07e871f4f3b3eb9f4ba361ac874d9b50","relation":"embedding","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"e9b252ff843abe280476eff7d28b33dc7dcccd530b3f4e0fa6834dad3a8062a7","source":"document:usage","target":"chunk:054fe28767c96ca856d71a857b4c0923255e093c83fce27a2697da6f647b99bb","relation":"contains","weight":0.5,"method":"unscored","model":null,"revision":null,"evidence":null,"overridden":false},{"id":"ea4fc28aa10e2dd5256f8c6ceba42c4411ee895cd75d1acec7bf0091ebd8ca6f","source":"document:overview","target":"document:agents","relation":"references","weight":0.63288844,"method":"lexical-proxy-v1","model":"feature-hash-v1","revision":null,"evidence":{"href":"ontology-document:agents","anchor":"Agent integration","context":"ing always validates frontmatter/resources. A runtime lists/reads skills and\nexports to `ROOT/skill-name`; it never edits harness config or executes resources.\n[Agent integration](ontology-document:agents) describes lint compatibility and MCP tools.\n\nBM25 always works offline. Default `feature-hash-v1` vectors are a lexical baseline,\nnot a semantic model. 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The original dated investigation is preserved as a historical research\n[attachment](ontology-asset:historical-research); its proposals describe the investigation, not today's CLI contract.\n\nRead the [usage guide](usage), [format guide](format) and [HTTP guide](http).\n","start_char":1538,"end_char":1586,"components":null},"overridden":false},{"id":"f8ec3e68f9b46fd7b6120826a7ce57bf32c5f84361483be4a866c34579c43513","source":"document:overview","target":"document:format","relation":"references","weight":0.5688212,"method":"lexical-proxy-v1","model":"feature-hash-v1","revision":null,"evidence":{"href":"ontology-document:format","anchor":"the format guide","context":"tent`, `graph`, `embeddings` and `packages` expose real data and\ndirectional reference evidence. 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