"Ingesting the messy world, spitting out clean context."
filegoblin is a single-binary CLI that "gobbles" almost any file or website — PDF, Word, Excel, PowerPoint, email, EPUB, HTML, whole repos — and converts it into clean, LLM-ready markdown.
- It just works.
filegoblin document.pdf > context.mdneeds no manual. Power flags exist, but the default path is the product. - Format invisibility. You shouldn't have to think about extensions; detection is extension-first with magic-byte fallback, so renamed and extension-less files route correctly.
- Fail loudly, never silently. An empty result exits non-zero, parse failures are reported in-band, and invalid flags are hard errors. An agent can trust every exit code.
- Local and private. Parsing and PII scrubbing happen entirely on your machine. No cloud APIs, no runtimes, no
pip install. - Not just a concatenator.
cat * > out.txtexists; filegoblin adds structure (real markdown from Word, one consistent table dialect, readability-grade web extraction), token budgeting, and redaction.
Every release comes in two flavours:
- with the PDF vision models for layout and reading order, formulas as LaTeX, tables, and OCR on scanned pages (~510 MB);
- lite, without them: the fast geometric converter, plus Apple Vision OCR on macOS.
Everything else is the same.
| Channel | Lite | With the models |
|---|---|---|
| GitHub release (latest) | filegoblin-<target> archive |
filegoblin-full-<target> archive: one binary with the models compiled in |
| PyPI | pip install filegoblin |
pip install filegoblin, then filegoblin --install-models (the filegoblin[models] packages aren't on PyPI yet) |
| npm | npm i -g filegoblin |
npm i -g filegoblin @filegoblin/models |
| Cargo | cargo install --git https://github.com/funkyfunc/filegoblin filegoblin |
then filegoblin --install-models |
Builds are published for macOS (Apple Silicon and Intel), Linux (x86_64 and aarch64), and Windows (x86_64).
Getting the models later.
filegoblin --install-modelsdownloads this version's model archive from the GitHub release, checks its SHA-256 and unpacks it into~/.filegoblin/models. You can pass a directory instead, and setFILEGOBLIN_MODELS_URLto point at an internal mirror. filegoblin only downloads when you ask it to.filegoblin --models-statusshows where each model was found.
Behind a corporate network. The npm model packages (and the PyPI ones, once they're published) carry the weights themselves, one package per model, with the two largest models in halves to stay under the registries' 100 MB per-file limit. They install from a company mirror (Artifactory, Nexus, CodeArtifact) like any other package, with no access to github.com.
Air-gapped machines. Copy the full download. Alternatively, put the filegoblin-vision-models.tar.gz archive on a shared read-only path and name it in FILEGOBLIN_MODELS. That variable takes a PATH-style list. Licenses are in LICENSES.md inside the archive.
From source — needs a Rust toolchain (1.87+):
cargo install --git https://github.com/funkyfunc/filegoblin filegoblinThat gives you everything in the "always on" list below, including web fetching and crawling. The optional engines are opt-in because each one pulls in a heavy dependency:
| Want | Add | Build needs |
|---|---|---|
| OCR on Linux/Windows | --features ocr |
just get-brains for the weights (macOS uses Apple Vision, no flag) |
| PDF vision models (layout, reading order, formulas → LaTeX, tables, scan OCR) | --features vision (on in release builds) |
The weights: filegoblin --install-models (the release's filegoblin-vision-models.tar.gz, SHA-256 checked) or just build-vision-models. filegoblin finds them in models/ next to the binary, in ~/.filegoblin/models, or in $FILEGOBLIN_MODELS. Add bundled-models with FILEGOBLIN_MODELS_DIR set to compile them into one binary |
| Audio/video transcription | --features transcribe |
cmake + a C/C++ compiler; downloads a ~574 MB model on first use |
| Metal-accelerated transcription (macOS only) | --features transcribe-metal |
Xcode Command Line Tools. Does not build on Linux/Windows |
| WASM plugins | --features wasm |
— |
BM25 search (--search) |
--features rag |
— |
# Example: a macOS build with GPU-accelerated transcription
cargo install --git https://github.com/funkyfunc/filegoblin filegoblin --features transcribe-metalWithout the models, --vision smart (the default) behaves exactly like the fast converter, with one note per run on stderr; --vision off silences it. With the models, it uses them only on the pages that need them. Anyone building from source can make a single self-contained binary with --features vision,bundled-models and FILEGOBLIN_MODELS_DIR set.
Measuring quality. just gauntlet runs the 29-case adversarial gauntlet
(just gauntlet off for the fast path). Each case gets pass/fail canaries and
a 0–100 quality score graded against hand-checked gold excerpts in
test_assets/gauntlet/gold/, plus checks that --format plain and
--chunk-size lose nothing. just gauntlet-compare A B shows what a change
did case by case. Results are in GAUNTLET_SCORECARD.md.
Note to Agent: Maintain this section as you add modules.
.
├── Cargo.toml (Virtual Workspace Root)
├── crates/
│ ├── core/ (filegoblin-core: The Engine & Parsers)
│ │ ├── Cargo.toml
│ │ ├── build.rs (Man page & shell completions)
│ │ └── src/
│ │ ├── lib.rs (Core Pipeline Logic)
│ │ ├── cli.rs (Clap Flag Definitions)
│ │ ├── formatters.rs (Output Assembly & Splitting)
│ │ ├── formatters/ (Plain text & specialized formatters)
│ │ ├── chunking/ (Layout-aware semantic chunking engine)
│ │ ├── curation/ (BM25 Search & Auto-Pruning)
│ │ ├── privacy_shield.rs (PII Redaction Engine)
│ │ ├── compressor/ (Token Compression Pipeline)
│ │ ├── parsers/ (File parsing engines; pdf_vision.rs = layout-guided PDF assembly)
│ │ ├── vision/ (Bundled ONNX models on rten: layout, formula, table, OCR; hayro rendering)
│ │ └── pipeline/ (Pipeline execution stages)
│ └── cli/ (filegoblin: The Headless Binary)
│ ├── Cargo.toml
│ └── src/main.rs (CLI Entry & Mascot)
│ └── walk.rs (--walk interactive picker)
├── npm/ (npx filegoblin Zero-Install Launcher)
│ ├── package.json
│ └── bin/filegoblin.js
└── .cargo/
└── config.toml (Aliases)
Some capabilities are gated behind opt-in Cargo features (built off by default to keep the binary lean). Feature-gated items are marked below.
- Multi-Format Output Engine (
--format markdown|plain|json/-f): GFM markdown (default), syntax-stripped plain text with space-aligned tables for vector embeddings (e.g. OpenAItext-embedding-3, BGE) and BM25 search, and structured JSON documents - Layout-Aware Semantic Chunking (
--chunk-size,--chunk-overlap,--chunk-strategy layout|tokens): splits along structural headings (H1 > H2 > H3), protects tables and code blocks from mid-block slicing, automatically replicates table headers across chunk splits, and embeds hierarchical outline breadcrumbs - Core Parsers (PDF, Word, Excel/CSV, PowerPoint, OpenDocument, Jupyter, Code, Email, RTF, HTML, EPUB) — always on
- PDF → markdown via
pdf_oxide: headings, pipe tables, reading order, hyphenation — with per-page scanned-vs-text classification - Scanned PDFs and images OCR automatically on macOS (Apple Vision, zero setup; parallelized page conversion, 30+ pages/s); Linux/Windows via
--features ocr - PDF vision pipeline (
--features vision+ model weights;--vision smart|all|off, defaultsmart): pages are rendered in pure Rust (hayro) and read by bundled, permissively licensed models onrten— no Python, system libraries or LLM at runtime. PP-DocLayoutV3 places every text-layer word in its region (title, heading, paragraph, caption, footnote, table, figure, running header/footer) in a learned reading order; display and inline formulas are re-read from the image as LaTeX (pix2text MFR); tables get rows/columns/spanning cells from Table Transformer and their cell values from the text layer; scanned pages, text regions the text layer leaves empty, and scans whose own OCR layer is too damaged to keep are read with PP-OCRv6 (medium).--vision smartdecides per page from cheap text-layer facts — scans, math fonts/symbols, column-aligned tables and forms, multi-column text, pictures, one- or two-page documents go through the models; plain single-column prose stays on the fast path. On the full olmOCR-bench (1,403 PDFs, official scorer) the models take filegoblin from 33.2% to 72.3% (arXiv math 0% → 84%, tables 26% → 75%, multi-column 46% → 71%, headers/footers 42% → 92%) - Geometric PDF page furniture removal: running headers/footers, page numbers, and line-number gutters (pleading paper, patents) are identified from position, cross-page recurrence, font size and alignment — before conversion — so prose, numbers and headings are never rewritten by text heuristics. Line-break hyphenation and words run together across visible gaps are repaired from page geometry plus the document's own vocabulary
- Semantic Chapter Boundaries: Automated detection and injection of thematic dividers and XML section markers (
<!-- filegoblin:section ... -->) - Word → real markdown: heading levels, nested lists, bold/italic, hyperlinks with URLs, and equations
- Word Resilient Fallback: Streaming OpenXML reader handling
<w:gridSpan>merged tables and<mc:AlternateContent>floating text boxes - Word Office Math (OMML):
<m:oMath>and<m:oMathPara>converted directly to standard LaTeX ($ ... $and$$ ... $$) with radicals, delimiters, matrices, and functions - Embedded Office Charts: PowerPoint (
.pptx) and Word (.docx) OpenXML DrawingML charts (c:chartSpace) deconstructed into clean Markdown pipe tables, Mermaidxychart-beta, and native Mermaidpie showDatadiagrams - DrawingML Connectors & SmartArt: PowerPoint and Word shape connections, flowcharts, and SmartArt process hierarchies compiled into Mermaid
flowchartdiagrams - Zero-install npm distribution: Execute via
npx filegoblinin environments without Rust toolchains - DOM-based HTML → markdown (readability-style content selection, link-farm pruning, preserved link URLs, title/source front-matter, ATX headings)
- One tabular dialect everywhere: standard markdown pipe tables across PDF, docx, pptx, OpenDocument, and CSV/XLSX
- Pure Rust Ruling-Line Table Extraction: Native port of
pdfplumber.tablevector ruling-line and intersection table algorithm, supporting collinear edge snapping, bounding box cycle reconstruction, multi-column merged headers, and fallback gutter detection - Trafilatura DOM Content Extraction: Native port of
trafilatura.coretext/link-density heuristics, boilerplate token pruning, syntax-highlighted code block preservation, and citation-link dampening - PowerPoint: manifest-true slide order + speaker notes + embedded chart tables
- Magic-byte format detection — extension-first, falling back to content signatures so renamed / extension-less files (including archives) still route correctly; legacy
.doc/.pptdetected with an actionable error - URL fetching routes by Content-Type: HTML → extractor, raw markdown/JSON/text → passthrough, binary documents (a PDF URL!) → the normal parser pipeline
-
--browser: render JavaScript-built pages (SPAs) with your installed Chrome/Edge/Brave — headless, throwaway profile, no bundled browser - Recursive crawling (
--horde) + GitHub repo cloning — on by default in the CLI binary (webfeature; disable with--no-default-features) - Fail-loud contract for agents: exit
4whenever nothing could be extracted, hard errors on invalid flags, quiet exit on broken pipes, explicit paths take precedence over piped stdin - MCP server (
--mcp): stdio Model Context Protocol server exposing aconverttool — plug filegoblin into Claude Desktop/Code with one config line - Subtitles (
.srt/.vtt): cue files → the same timestamped transcript markdown whisper produces, with speaker labels and rolling-caption dedup - RSS/Atom feeds: a feed URL or saved feed file → a clean markdown list of entries
- Conversion cache: repeat URL fetches and OCR/transcription results come back instantly (
~/.filegoblin/cache,--no-cacheto bypass) - Audio/video transcription (
--features transcribe; addtranscribe-metalon macOS for GPU): mp3/m4a/wav/flac/ogg/opus/mp4/mov/webm/mkv (audio track) → timestamped transcript markdown via whisper.cpp, on-device, model auto-downloads once - Golden-file test suite: end-to-end snapshots of real fixture output per format (
cargo insta reviewto inspect changes) - WASM Plugin Extensibility (
--plugin) — requires--features wasm - Semantic BM25 Search with Relevance Scores (
--search) — requires--features rag - Conversion cache management:
--no-cacheto bypass,--cache-clearto empty it - Glob Filtering (
--include,--exclude) & Depth Control (--depth) - Token Estimation (
--tokens), Token-Only Mode (--tokens-only) - Token Compression (
--compress safe|contextual) - Auto-Pruning to Token Budgets (
--max-tokens) - Manifest Table of Contents (
--manifest) - Git-Aware Diffing (
--git-diff) with Unified Diff Output (--diff-format) - Rust Symbol Extraction (
--extract symbols) via tree-sitter - Local PII / Secret Redaction (
--scrub) — deterministic + entropy heuristics - Output Splitting (
--split), Chunking (--chunk), JSON (--json) - Clipboard Ingestion (
--clipboard), Clipboard Output (--copy), OS Open (--open), File Write (--write) - Heuristic Project Summaries (
--summary) - Out-of-core streaming for the default dump path (flat RAM footprint)
- Pipeline-Safe Streams (
-qfor clean stdout)
- Parsers:
pdf_oxide,docx-rs,tree-sitter,quick-xml,calamine,scraper(Statically Linked). - OCR built in on macOS: Apple Vision (
objc2) with no weights or flags — scanned PDFs and images just work. Linux gets pure-Rustocrs/rteninference behind--features ocr. - WASM Extensibility:
wasmtimefor pure-rust statically-linked component execution. - Privacy First: 100% local PII/secret scrubbing — deterministic patterns (SSN, email, cloud/token prefixes) plus a Shannon-entropy tier that redacts high-entropy secrets. No model weights, no data egress.
- Structural Minification: Tree-sitter powered
--extract symbolsmode for Rust source.
| Source | Path Example | Parser Used | Output Format |
|---|---|---|---|
~/receipt.pdf |
PdfGobbler |
Markdown (headings, pipe tables, OCR for scans) | |
| Word | ~/notes.docx |
OfficeGobbler |
Markdown (headings, lists, emphasis, links, equations, charts) |
| Excel/CSV | ~/data.xlsx |
SheetGobbler |
Markdown pipe tables (header row named) |
| PowerPoint | ~/deck.pptx |
PptxGobbler |
Per-slide markdown + speaker notes + embedded charts |
| OpenDocument | ~/doc.odt, ~/deck.odp |
OpenDocGobbler |
Markdown (headings, lists, tables) |
| Source Code | ~/project/src |
CodeGobbler |
Full content (or --extract symbols) |
~/thread.eml, ~/inbox.mbox |
EmailGobbler |
Header block + markdown body + attachment list | |
| RTF | ~/notes.rtf |
RtfGobbler |
Plain text |
| Images | ~/screenshot.png, ~/IMG_0042.HEIC |
OcrGobbler (built-in on macOS) |
OCR Text |
| Subtitles | ~/movie.srt, ~/talk.vtt |
SubtitleGobbler |
Timestamped transcript |
| RSS/Atom | https://hnrss.org/frontpage |
FeedGobbler |
Entry list (title, link, date, body) |
| Webpages | https://news.ycombinator.com |
HtmlGobbler (default build) |
DOM extraction + front-matter |
- Semantic Search: BM25 relevance scoring via
tantivywith zero model weights. - Manifest TOC: Auto-generated table of contents with per-file token counts.
filegoblin maintains a strict zero-dependency profile for the end user. However, building the project requires assembling several logic assets.
build.rs generates the manpage and shell completions into target/generated/. The .rten tensor weights for pure-Rust OCR fallback on Linux/Windows are not downloaded automatically — fetch them with the provided justfile when building with --features ocr:
just get-brains(On macOS, OCR uses Apple's Vision framework natively; no weights needed.)
Basic Gobble (Files):
# Output a single file to stdout
filegoblin my_notes.pdf > context.md
# Merge multiple files natively
filegoblin src/main.rs src/lib.rs README.md --write context.mdURL Ingestion (Web):
filegoblin https://example.com/api-docs > context.mdWeb Horde (Split into mapping directory):
filegoblin https://bettercli.org/ --horde --split
# Translates to -> ./bettercli.org_gobbled/Privacy Shield (Redact PII & Secrets locally):
# Typed redaction (default): values become [EMAIL], [AWS_KEY], [CREDIT_CARD], …
filegoblin ./src/api_keys.ts --scrub > safe_context.md
# Pipe a log through it as a pre-flight gate before any cloud LLM
cat prod.log | filegoblin - --scrub --scrub-style plain
# CI / pre-commit guard: non-zero exit if anything sensitive is found
git ls-files -z | xargs -0 cat | filegoblin - --scrub --scrub-fail -q || echo "secrets detected!"
# Exempt a known-public value, or add your own pattern via ./.filegoblin-scrub.json
filegoblin ./config --horde --scrub --scrub-allow "support@example.com"
# Consistent pseudonyms: the same value maps to [EMAIL_1] everywhere, so an LLM can
# still tell that an entity recurs — without ever seeing it.
cat ticket.txt | filegoblin - --scrub --scrub-style pseudonymDetection is 100% local with zero model weights: validated rules (Luhn for cards, range
checks for SSN, mod-97 for IBAN) plus cloud/token prefixes (AWS, GitHub, Stripe, Slack, Google,
OpenAI, JWT, private-key blocks, connection strings) and a Shannon-entropy catch-all for unknown
secrets. A value-free audit tally is printed to stderr (🛡️ Privacy Shield redacted 4 item(s): …).
An optional .filegoblin-scrub.json supplies an allowlist and custom patterns.
What filegoblin does NOT do: maintain a secrets blocklist. It converts exactly what you point it at — including a
.envfile if you name one. Deciding what's sensitive is yours: the directory walker respects.gitignore(where secrets usually already live),--excludefilters by glob, and--scrubredacts what does get through.
Machine Contract (--json) — extract any file to clean text for agents/scripts:
# One weird binary format → clean text + metadata, in a single call
filegoblin report.pdf --json | jq '.[0] | {format, tokens}'
# → { "format": "pdf", "tokens": 1234 }
filegoblin ./src/ --horde -q --json | jq '.[].path'--json emits a deterministic, alphabetically-sorted array. A successful record is
{ "path", "format", "tokens", "content" } (format is a coarse type like pdf,
spreadsheet, code, image, text; tokens is an LLM token estimate). A file that
fails to parse appears in-band as { "path", "format", "error" } rather than silently
vanishing — so an agent can trust that every input is accounted for. Exit codes: 0 success,
4 when nothing could be extracted (empty result vs. total failure), 3 when
--scrub --scrub-fail redacted something.
Multi-Format Output (--format markdown|plain|json / -f):
# Default GFM markdown with tables, math, and code fences
filegoblin document.pdf -f markdown > doc.md
# Strip markdown punctuation for dense vector embeddings & BM25 search
# (Pipe tables become space-aligned text columns; headings & math stripped of tokens)
filegoblin document.pdf -f plain > embedding_input.txt
# Structured JSON document array for agent pipelines
filegoblin document.pdf -f json > document.jsonLayout-Aware Semantic Chunking (--chunk-size, --chunk-overlap, --chunk-strategy):
# Split along structural boundaries (H1/H2/H3, tables, code blocks) with 512 token budget
# Replicates table headers across chunk boundaries and embeds hierarchical breadcrumbs
filegoblin document.pdf --chunk-size 512 > chunks.md
# Structured JSON chunks with breadcrumbs and token estimates for RAG vectors
filegoblin document.pdf -f json --chunk-size 512 --chunk-overlap 64 > rag_chunks.jsonToken Compression (Reduce LLM context sizes):
# Strip code comments, minify JSON/HTML, collapse whitespace
filegoblin ./src/main.rs --compress contextual > context.mdMulti-Part Splitting (Chunk output by tokens):
# Automatically break massive repositories into `partN` files at a 50k token threshold
filegoblin ./src/ --horde --chunk 50kPipe from stdin:
curl -s "https://api.github.com/users/octocat" | filegoblin -q --jsonClipboard Ingestion:
# Read contents directly from your OS clipboard
filegoblin --clipboard > context.mdDevelopment & Intelligence:
# Get a heuristic overview of the project with token counts
filegoblin ./src/ --summary --tokensFiltering & Exclusion:
# Only Rust files, excluding tests and generated code
filegoblin ./src/ --horde --include "*.rs" --exclude "*test*" --exclude "*generated*"
# Limit crawl depth to top-level files only
filegoblin ./src/ --horde --depth 1Manifest / Table of Contents:
# Prepend a TOC with file paths and token counts
filegoblin ./src/ --horde --include "*.rs" --manifestToken-Only Mode (Scripting):
# Print just the token count, no content — ideal for CI/scripts
filegoblin ./src/ --horde --tokens-onlySemantic Search with Relevance Scores:
# Search across a codebase and see BM25 relevance scores
filegoblin ./src/ --horde --search "authentication"Git-Diff Mode (Only Changed Files):
# Ingest only files changed since last commit
filegoblin . --horde --git-diff HEAD~1
# Show unified diffs instead of full file contents
filegoblin . --horde --git-diff HEAD~1 --diff-formatScoping a crawl (--depth, --include, --exclude):
These apply to both halves of --horde — local file paths, and the URL path
when crawling. Out-of-scope pages are never fetched, so scoping saves requests
rather than just trimming the output.
# Only the guide section, and never the changelog
filegoblin https://docs.example.com --horde --include "/guide/*" --exclude "*changelog*"
# The seed page and whatever it links to directly — nothing further
filegoblin https://docs.example.com --horde --depth 1
# Shorthand: a bare word matches anywhere in the path
filegoblin https://docs.example.com --horde --include guideThe seed URL is always fetched, so --include "/guide/*" starting at / still
works. --depth 0 is the seed alone.
Walk and pick (--walk) — for when you don't want the whole site:
Pointing --horde at a website converts everything it can reach: the changelog,
the 2019 blog archive, every language variant. --walk opens a picker instead —
read each page, follow its links, mark what you want, convert only that.
filegoblin --walk https://docs.example.com > context.md
filegoblin --walk ./my-repo > context.md https://docs.example.com/guide/ 8.4k tok ↳1
# Getting Started
Install the CLI, then point it at a file. Every
conversion happens locally on your machine.
─ pick ──────────────────────────────────────────
❯ ◉ this page — Getting Started
○ Next: Configuration
○ API Reference
1 selected ↑↓ move · space keep · ⏎ follow · b back · d done · q quit
The preview is the actual converted markdown, so you're judging what you'd get, not a guess. Links are scoped to the same origin (host and port), and selecting a directory includes everything beneath it. Pages you revisit come from the conversion cache, so backing up is instant.
The picker draws on stderr and the markdown goes to stdout, so redirecting works normally. It needs a terminal — piping without one is a clear error rather than a hang, and every other invocation is unchanged, so scripts, agents and the MCP server are unaffected.
MCP Server (Claude Desktop / Claude Code):
filegoblin --mcp speaks the Model Context Protocol over stdio, exposing a single
convert tool that takes a file path, directory, or URL.
For Claude Code:
claude mcp add filegoblin -- filegoblin --mcpFor Claude Desktop, add this to claude_desktop_config.json
(macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):
{
"mcpServers": {
"filegoblin": {
"command": "filegoblin",
"args": ["--mcp"]
}
}
}Use an absolute path to the binary if it isn't on the PATH your client sees. To let
the tool reach services on your own machine, add
"env": { "FILEGOBLIN_ALLOW_LOCAL_URLS": "1" } — see the security note below for
why that is off by default.
- Unified Output Formats (
--format):--format markdown/--format md(default): Clean, token-optimized Markdown for LLM context windows.--format plain(or--plain): Syntax-stripped prose for text-embedding models (OpenAI, Cohere, Voyage) and token counters.--format json(or--json): Structured AST array of typed semantic blocks (heading,paragraph,table,code,math,callout) with bounding box and page metadata for RAG ingestors.
- Semantic Section Chunking (
--chunk-tokens,--chunk-by):- Chunk documents into LLM context-ready token budgets (
--chunk-tokens 512) without severing tables, code blocks, or mathematical equations across chunk boundaries. - Boundary-aware splitting (
--chunk-by section|chapter|page).
- Chunk documents into LLM context-ready token budgets (
- Multi-Channel Distribution:
- PyPI (
pip install filegoblin): Python wheels built via Maturin for instant drop-in use in LangChain, LlamaIndex, and PyTorch pipelines. - Crates.io (
cargo install filegoblin): Publish CLI binary andfilegoblin-corelibrary crate. - Homebrew Tap (
brew install funkyfunc/tap/filegoblin): Zero-dependency native macOS installation. - Precompiled Binary Releases: Automated cross-platform release archives (Linux x64/arm64, macOS Apple Silicon/Intel, Windows x64) via
cargo-dist.
- PyPI (
- URLs pointing at private addresses are refused. Loopback, private ranges,
link-local (including the cloud metadata endpoint
169.254.169.254), and any hostname that resolves to one are blocked — on the initial URL, on every redirect hop, and inside the DNS resolver itself, so a name that resolves differently between the check and the connection (DNS rebinding) is refused too. SetFILEGOBLIN_ALLOW_LOCAL_URLS=1to allow them — reasonable for converting your own dev server, and worth thinking about before enabling it for the MCP server, where the URL comes from a model. --browseris a wider surface. Chrome does not honour those redirect guards, so a page it renders can redirect somewhere filegoblin would refuse to fetch directly. The initial URL is still validated, the profile is a throwaway temp dir, and the mode is strictly opt-in.- Archives and Office files have decompression caps. Zip, tar.gz, and the
zip-container formats (
.docx/.pptx/.odt/.epub) bound what they inflate and refuse entries whose paths escape the extraction directory.
All code must pass the ritual before being merged:
cargo horde-check # clippy -D warnings + fmt --check + test, via .cargo/config.tomlGolden-file tests live in crates/cli/tests/ and use insta snapshots; run
cargo insta review to inspect a diff before accepting it.
filegoblin embraces the Mischievous Librarian archetype. We don't "process" data; we feast on it.