# DEEPBOM **Category:** 💻 Developer Tools **Repository:** https://github.com/JunHwan-Kwon/deepbom **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/deepbom ## Description Browser-local and CLI static evidence for deployed AI model artifacts. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "deepbom": { "command": "npx", "args": ["-y","deepbom"] } } ``` ## Documentation & README # DEEPBOM DEEPBOM is a local static analyzer for deployed AI model artifacts. It audits serialized graph, tensor, quantization, memory, compatibility, and ML-BOM evidence without uploading model bytes. It is a multi-format artifact-evidence producer, an external-interface contract verifier, and a bounded BOM-to-artifact reconciliation tool. It is not an official reference implementation, a complete BOM validator, or a regulatory compliance verifier. The public source distribution covers TFLite, ONNX, GGUF, SafeTensors, Core ML, and bounded ExecuTorch artifacts. Findings distinguish observed and derived artifact facts from predicted compatibility, imported runtime evidence, and values that cannot be assessed statically. ## Quick start Run the published CLI without cloning the repository (Node.js 20 or newer): ```bash npx deepbom audit "https://raw.githubusercontent.com/JunHwan-Kwon/deepbom/main/web/samples/gpu_partition_probe.onnx#sha256=82a2feef00eb6ab03d82f2b30cd17f4d826e2d8307cb059eccd6a0f3120059b2" ``` The pinned expected values and independent verifier are in [`examples/expected-output`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/examples/expected-output/README.md). Source builds require Rust and, for the Python channel, Python 3.9 or newer; maintainer setup is documented separately below. ## Assistant integration Install the repository-local Agent Skill after previewing the managed files: ```bash npx -y deepbom@1.109.0 integrate codex npx -y deepbom@1.109.0 integrate codex --apply npx -y deepbom@1.109.0 integrate claude-code npx -y deepbom@1.109.0 integrate claude-code --apply ``` The Skill lets a local agent select a contract-compatible analyzer for a supported deployment-artifact question. It does not create or call a hosted analysis server. See [the agent setup guide](https://deepbom.org/for-agents/), the [machine-readable capability contract](https://deepbom.org/agent-capabilities.json), and the [independent engine/Agent/evidence version policy](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/AGENT_CONTRACT_VERSIONING.md). The installed Skill first checks `DEEPBOM_BIN`, its package-bundled analyzer, and a `deepbom` already on `PATH`. It accepts a runtime only after the declared `deepbom.agent_contract.v1` and evidence contract match and its self-test binds the exact observed engine version. The verifier does not contact npm unless `--allow-download` is explicitly supplied. This keeps a blocked registry lookup from being mistaken for a failed artifact analysis. For persistent tool-call access, run the same local analyzer as an MCP server over stdio: ```bash npx -y deepbom@1.109.0 mcp ``` It exposes `deepbom_capabilities`, `deepbom_audit`, `deepbom_diff`, and `deepbom_explain_rule` without uploading artifact bytes or using a hosted analysis endpoint. Audit calls default to a bounded human summary; detailed formats and large-model scan depth are explicit. Local paths are restricted to the launch directory unless `DEEPBOM_MCP_ALLOWED_ROOTS` is configured. Agent-facing usage guidance is in [the DEEPBOM skill](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/skills/deepbom/SKILL.md). Claude Desktop users can instead install the version-matched `deepbom-1.109.0.mcpb` asset from the corresponding GitHub Release. The bundle contains the same CLI and WASM bytes as the npm channel and asks the user to select the only local directory it may read. For a single attachment in ChatGPT, connect the separately bounded Streamable HTTP endpoint at `https://deepbom.org/mcp`. Its browser component performs the analysis in the ChatGPT sandbox and returns a bounded, hash-bound result; the DEEPBOM service does not fetch or retain model bytes. This path is ready for developer-mode review, while public ChatGPT discovery remains subject to OpenAI review. See [the ChatGPT integration guide](https://deepbom.org/chatgpt/). Use the local CLI or stdio MCP for confidential or large artifacts, package and sidecar closure, complete exports, and repeated automation. For private testing on a Claude host that supports remote MCP Apps, add the separate `https://deepbom.org/mcp/claude` connector. It opens an explicit file picker inside the MCP App; it does not automatically read Claude attachments. The selected bytes remain in the browser sandbox while a bounded result is returned to the conversation. Public Claude discovery remains subject to Anthropic review. See [the Claude integration guide](https://deepbom.org/claude/) and use the local MCPB or CLI when derived model facts must also remain local. The repository root is also a portable Agent Plugins package: `plugin.json` identifies the Skill and `mcp.json` declares the bounded ChatGPT attachment endpoint. The compatibility manifest under `.codex-plugin/` carries the same identity for clients that have not moved to the portable format. These files prepare installation and review; they do not imply a public directory listing or platform approval. Account-owned ChatGPT, Claude, Codex, and search-indexing steps are kept in the [agent distribution operations checklist](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/AGENT_DISTRIBUTION_OPERATIONS.md). Verified release channels expose the same analysis implementation: ```bash npx deepbom audit model.onnx --format cyclonedx npx deepbom audit model.onnx --format sarif --output deepbom.sarif --gate defects deepbom capabilities --compact deepbom self-test deepbom audit model.onnx --list-sections --compact deepbom audit model.onnx --section quantization,memory --compact deepbom explain-rule onnx.conv.output-shape python -m pip install deepbom cargo install deepbom deepbom audit model.gguf --compact deepbom verify model.tflite --contract production-interface.json deepbom verify model.onnx --bom supplied.cdx.json --render markdown deepbom contract capture model.onnx -o baseline.interface-contract.json deepbom diff baseline.gguf candidate.gguf --tensors --render markdown deepbom capabilities --format agent-text deepbom explore model.tflite deepbom placement model.tflite --profiles xnnpack_cpu,tflite_coreml_delegate,litert_qualcomm_qnn deepbom graph model.onnx --format json --output artifact-graph.json deepbom audit model.onnx --section model_ir --compact deepbom visualize model.onnx --view all --orientation portrait -o model-views.zip deepbom model-summary model.onnx --format table deepbom model-summary model.gguf --level storage --format markdown deepbom audit model.onnx --conversion-receipt conversion-receipt.json --format cyclonedx ``` The default is a terminal-sized evidence summary. `--json` and `--compact` expose complete format evidence; `--format envelope` provides the canonical cross-format contract; CycloneDX 1.7 and OASIS SARIF 2.1.0 are standard projections. `--policy-output` records a hash-bound gate result when `--fail-on` is selected. `--review-policy` adds identity-scoped, expiring exceptions and keeps execution, coverage, and finding-policy states independent. See [`docs/CLI_AUTOMATION.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/CLI_AUTOMATION.md). The complete option inventory is generated from the executable in [`docs/CLI_REFERENCE.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/CLI_REFERENCE.md). CycloneDX 1.7 consumers should read the analyzed model from `metadata.component` and additional inventory members from `components[]`. DEEPBOM does not duplicate the BOM root into the additional-components array. The graph JSON output includes the evidence-preserving `deepbom.artifact_ir.v2` ledger and a deterministic `deepbom.graph_ir.v1` visualization compatibility projection. Serialized graph, storage topology, architecture grouping, scoped quantization, static placement, and imported runtime evidence remain separate. Method `2.2.0` also preserves an optional, output-bound conversion receipt without promoting declared converter execution to observed evidence. It materializes exactly decoded TFLite subgraphs, ONNX nested graphs/local functions, and ExecuTorch primary plans without flattening conditional scopes. Runtime-node fusion is reconciled only from artifact-bound subject references or primary native op indices; names are never guessed. `graph_ir.v1` remains primary-scope-only for legacy consumers. The v2 JSON Schema is published at [`docs/schemas/deepbom-artifact-ir-v2.schema.json`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/schemas/deepbom-artifact-ir-v2.schema.json) and at `https://deepbom.org/schemas/deepbom-artifact-ir-v2.schema.json`. The conversion receipt schema is published at [`docs/schemas/deepbom-conversion-receipt-v1.schema.json`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/schemas/deepbom-conversion-receipt-v1.schema.json). The additive preview `deepbom.model_ir.v1` projects those frozen artifact facts into a format-neutral program, logical-value, storage, binding, quantization, architecture, and static/runtime evidence vocabulary. It preserves source, dependency, deterministic display, and observed runtime order as different fields. `visualize` consumes only this IR and creates deterministic monochrome ISO A4 SVG pages, 300-DPI black-and-white PNG derivatives, caption sidecars, and a Word insertion manifest. These are traceable engineering documents, not standard-conformance or regulatory-approval determinations. See [`docs/MODEL_IR_V1.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/MODEL_IR_V1.md). `model-summary` is the shared Keras-summary-like projection for every supported adapter. It emits operation rows when a serialized program exists, storage rows for graphless weight containers, and an explicit identity-only result for safe envelopes. It never relabels serialized constants as trainable parameters or a deterministic display order as observed runtime. JSON consumers can use `deepbom.model_summary.v1`, published in [`docs/schemas/deepbom-model-summary-v1.schema.json`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/schemas/deepbom-model-summary-v1.schema.json). Public product output uses CycloneDX 1.7. `verify` compares the serialized external tensor ABI with a supplied, artifact-bound production declaration. `diff` uses the canonical deterministic multi-target TFLite deployment-delta ledger, and `explore` exposes the existing WASM redesign Pareto search without claiming trained-model accuracy. A strict custom TFLite target can be bound with `--target-profile profile.json`; the CLI records both the source-file SHA-256 and the resolved Rust profile SHA-256. Accelerator evidence is separate from that CPU cost profile. Source-pinned TFLite Core ML and LiteRT Qualcomm QNN profiles, Core ML MLComputePlan, Edge TPU and Qualcomm compiler reports, TensorRT parser/engine evidence, and NVIDIA host profiles use a shared staged binding without promoting static or compiled evidence to observed execution. `placement` compares any available profiles over one conserved graph ledger without inventing backend priority. The Cargo launcher downloads only the engine matching its exact package version and platform from the corresponding immutable GitHub Release. It validates the release matrix, byte lengths, and SHA-256 digests before caching or execution. Build the npm, standalone, Python, and Cargo launcher channels: ```bash npm run build:channels npm run check:public-package-boundary npm run check:channels -- --no-build ``` Run the public correctness gates: ```bash npm run check:cli npm run check:cli-docs npm run check:cli-automation npm run check:formats npm run check:rust ``` The browser workbench is available at [deepbom.org](https://deepbom.org/). ## Evidence scope ### Format maturity Accepted extensions do not imply equal analytical depth. TFLite and ONNX have stable executable-graph contracts. GGUF and SafeTensors have stable container, storage, and architecture contracts but do not serialize an executable graph. Core ML and ExecuTorch analysis remains preview and fixture-bounded. The exact parser, graph, quantization, and placement status for every format is available from `deepbom capabilities --compact` under `public_product_contracts`. | Format | Public static evidence | | --- | --- | | TFLite | FlatBuffer graph and tensor contracts, quantization arithmetic, weight integrity, memory projections, accumulator proofs, redesign candidates, and source-bounded delegation predictions | | ONNX | Protobuf graph, initializer and external-data contracts, symbolic shape inference, operation cost, Q/DQ and affine quantization, and provider-compatible evidence envelopes | | GGUF | Container and tensor-directory integrity, quantization encoding inventory, architecture metadata, and bounded LLM memory scenarios | | SafeTensors | Tensor-directory and sharding integrity, configuration-bound architecture contracts, AWQ/GPTQ/HQQ/compressed-tensors metadata, and bounded LLM memory scenarios | | Core ML | NeuralNetwork and ML Program serialized graphs, tensor/weight encodings, deployment floor, and imported compute-plan evidence boundaries | | ExecuTorch | Bounded ET12/FT01 plans, source-bound portable calls and processed payload identities, plus optional selected-build/backend/operator/binary attestation; execution remains external | | GraphDef / SavedModel | Bounded serialized nodes, data/control dependencies, first-MetaGraph SignatureDef tensor contracts, and hash-bound package membership; no graph execution | | Keras / PT2 | Bounded declarative config/JSON dependency graphs without object construction, pickle loading, lowering, native code, or runtime-order claims | | HDF5 / PyTorch checkpoint | Safe-envelope, archive, storage-member, and pickle-risk inventory only; no object graph or executable-model claim | Static compatibility does not establish observed execution-provider assignment, device latency, task accuracy, clinical validity, or release readiness. Runtime claims require an identity-bound runtime capture. The detailed format and accelerator boundary is maintained in [`docs/SUPPORT_MATRIX.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/SUPPORT_MATRIX.md). Bugs can be reported without sharing model bytes using [`docs/MODEL_FREE_BUG_REPORTING.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/MODEL_FREE_BUG_REPORTING.md). ## Distribution boundary This repository is generated from an exact reviewed allowlist. Private rulepack generators, hosted-service infrastructure, and unreleased research modules are not included. The enforceable boundary and export verification method are documented in [`docs/PUBLIC_PRIVATE_BOUNDARY.md`](https://github.com/JunHwan-Kwon/deepbom/blob/HEAD/docs/PUBLIC_PRIVATE_BOUNDARY.md). ## License and citation The public source and release-channel packages are licensed under the Apache License 2.0. Third-party model artifacts retain their declared licenses. Please cite: > Kwon, J. (2026). DEEPBOM: Browser-Native Static Analysis of On-Device Neural > Network Deployment Artifacts [Computer software]. Zenodo. > https://doi.org/10.5281/zenodo.21834508 ## Optional metadata and OMOP connections After inspecting a model, create a model-bound input with `deepbom audit model.onnx --metadata-template omop -o metadata.json`. Fill the selected OMOP 5.4/5.5 `CDM_SOURCE` row and institution/release identifiers, then use `--metadata metadata.json --evidence-files ./evidence --section evidence_link_ir --json`. A generic template connects non-OMOP datasets, cohorts, code, runs and reports through the same IR. The Web **Metadata** workspace provides import, record creation, an interactive relationship diagram and JSON/CycloneDX downloads. Local MCP `deepbom_audit` accepts `metadata_template`, `metadata` and `evidence_files`; ChatGPT/Claude widgets share only explicitly requested counts and digests. Connections remain declarations: file hashes and reference consistency do not authenticate lineage or clinical suitability. See the [OMOP guide](https://deepbom.org/guides/omop-metadata/) and [common IR contract](https://github.com/JunHwan-Kwon/deepbom/blob/main/docs/EVIDENCE_LINK_IR.md).