Articulate

A local writing-quality and AI-tell detection and editing tool. It flags the prose devices and machine-writing tells that make text read as generated, scores how machine-textured a passage is, and (with an LLM backend) rewrites prose to a plain, skilled standard. The core runs standard-library-only with no network call. Detection quality and writing quality are the goals; a detector score is a benchmark and a byproduct, never something the tool optimizes toward, and it is not an evasion tool.

Documentation

Full docs are in docs/: getting started, a thorough walkthrough, the feature reference, the CLI reference, and the boundaries that say what a verdict and a receipt mean and what they never claim.

What it does

• Detect. Flags banned rhetorical devices (antithesis including keyword-free parallel-negation contrast pairs, corrective negation, rule-of-three, em-dashes, filler intensifiers, corporate verbs), current frontier-model register, marketing, and email/blog tells, and Williams/Orwell signals (expletive openers, nominalization density, passive voice, adverb density, cadence uniformity, opener repetition). Emits a graded 0-100 texture score and a clean/flagged gate.
• Adapt by register. A profile system (procedure, commit, research, readme, essay, narrative, and more) sets which findings block. Fiction gates nothing; procedures and essays gate strictly. Profiles resolve from --profile, an in-file writing-profile: tag, or the file path.
• Choose a mode or a genre. A writing mode crosses a domain register with an articulation need, such as memo/argue or technical-docs/explain. The genre axis reads narrative and expressive prose by its own convention: literary-fiction, genre-fiction, ya-fiction, memoir, screenplay, poetry. Under a fiction genre, quoted speech is masked out of the device passes so a character's line is never scored as the author's prose; the craft devices report but never block; and a report-only lexicon flags generation artifacts such as the somatic cliche or the "could not help but" reflexive. Screenplay classifies Fountain roles first, so only action lines face the device gate. Poetry reads by the line and drops the craft-device categories from its report. Run articulate modes to list them.
• Edit. judge reads the judgment-level failures a regex cannot see (confident emptiness, vague abstraction, hedging with no position, weak verbs). fix rewrites to the standard, self-checked against the detector. polish loops until five qualities (concreteness, commitment, economy, rhythm, a restatable fact per paragraph) clear a bar. Gated on writing quality, never a detector score. Needs an LLM backend (local model or the claude CLI).

Use

# lint (exit 1 when blocked under the file's profile)
python -m articulate.cli check path/to/doc.md --gate
python -m articulate.cli check essay.md --profile essay --verbose
echo "some prose" | python -m articulate.cli score

# library
python -c "import articulate; print(articulate.check_text('...', profile=articulate.profiles.load('research'))['gate'])"

# receipt: a re-derivable verdict (Match / Drift / Unverifiable)
python -m articulate.cli receipt doc.md --profile research > doc.receipt.json
python -m articulate.cli verify doc.receipt.json doc.md   # replay; exit 0/1/2

# content-free audit receipt: replayable, but stores no verbatim text (drop or hash
# the matched substring). For a team that must retain a record without the source.
python -m articulate.cli receipt doc.md --redact drop --reviewer alice > receipts/doc.json
python -m articulate.cli check doc.md --content-free --sarif > doc.sarif  # no substrings

# audit: query committed receipts locally (no server), and re-verify they still hold
python -m articulate.cli audit receipts/                 # recorded verdicts, blocked rules
python -m articulate.cli audit receipts/ --reverify --gate   # exit 1 if a source drifted

# SARIF for CI (GitHub Code Scanning, Azure, reviewdog)
python -m articulate.cli check src/**/*.md --sarif > articulate.sarif

# LSP server (inline squiggles in VS Code, JetBrains via LSP4IJ, Neovim). Stdlib
# only, no dependency. Point your editor's LSP client at:
python -m articulate.lsp_server

# benchmark (regression-gated corpus) and MCP server
python -m articulate.bench
python -m articulate.mcp_server

Editor setup

The LSP server speaks standard LSP over stdio, so any LSP client can drive it. A minimal Neovim registration:

vim.lsp.start({ name = "articulate", cmd = { "python", "-m", "articulate.lsp_server" },
  filetypes = { "markdown", "text", "tex" } })

For VS Code, a thin client that launches the same command as a LanguageClient is all that is needed; no server code lives in the extension.

Scientific and mathematical writing

academic/prove and science-writing/explain target hard technical exposition: stating the idea before the formalism, keeping a roadmap, defining each symbol once. The proof mode does not rewrite by default, because a wrong change to a quantifier order or an inequality direction changes a theorem; it routes to --judge, and --fix is opt-in. On a .tex file the editor masks every math span before a rewrite and splices it back byte for byte, so a formula is never altered.

The boundary is fixed and load-bearing: a clean gate, a low texture score, or a Match receipt means the prose was screened under a named ruleset. It says nothing about whether the theorem is true. A clearly written proof can still be false, and Articulate never checks the mathematics. Correctness comes from referees and proof assistants (Lean, Coq, Isabelle), never from this tool.

Privacy

The detector never touches the network. The editor layer defaults to a local model where configured, and an --offline mode is on the roadmap for air-gapped use. A content-free audit receipt keeps no verbatim text: it drops the matched substring and the exact offsets, keeping only which rule fired, its tier and category, and the line. A team can retain and replay a record without storing the sensitive source. Content-free is not zero-leakage: which rules fired and the line remain, which for a closed-vocabulary rule narrows the flagged word to that rule's small public candidate set. The hash mode keeps a sha256 for an equality check against a known string, so it is dictionary-reversible for those closed-vocabulary rules; use drop when the flagged word must stay secret. An auto-filled reviewer (from $GITHUB_ACTOR) records CI attribution, and a named human sign-off needs an explicit --reviewer. Committed receipts live as long as the repo, with no automatic expiry (bounded retention is a later self-hosted tier). The full documentation covers each surface and the boundaries in depth.

Status

Pre-1.0. The core detector, profile system, writing modes (including the science modes for proofs and technical exposition), the genre axis (fiction, memoir, screenplay, poetry), the editor injection boundary, per-span mixed-authorship verdicts, a sub-threshold "unverifiable" calibration, binary fail-closed input guards, the benchmark, the editor layer, the CLI, the LSP and SARIF surfaces, receipts, the content-free audit receipt, and the MCP server are built into this one package. A local-model editor backend, a labeled non-native corpus for a fairness check, and PyPI packaging are on the roadmap.