Lint Jev and Luna questions before you send them. DecisionLint checks the
questions you write for a decision model: TypeSafe's Jev, OpenAI's Decisions
API (gpt-6-luna), and every platform that serves either shape, from
OpenRouter and Vercel AI Gateway to a llama.cpp on your own machine. DecisionLint reads
them out of your JSON and your code, and reports the ones written in a way
that is measured to produce bad answers. Linting makes no API calls and needs
no key. One optional command asks the model itself to check a file, using
your own key, at whichever platform you send it to.
Lint Jev and Luna decision-model questions, or ask the model to check them with your key.
2026-11-10
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Lints as you type in JSON, JavaScript, TypeScript, Python, Rust and Go.
Fixes on the lightbulb for the mechanical mistakes, and a way to silence
a finding you disagree with.
The same rules in CI, from a command line, and for AI agents, from an
MCP server. One settings file covers all three.
Every platform, one set of rules. TypeSafe's /v1/systemone shape and
OpenAI's /v1/decisions shape, predicate, choices and levels, are
read into the same rules, and the AI SDK's decide() call for either.
The limits a request is held to are the platform's own, read from the
model id's spelling, a URL in the file or a target setting: Vercel's 255
choices, LiteLLM's 128 questions, none where a platform publishes none.
Two optional commands that use your own key: one asks the model
whether your questions have problems a text pattern cannot see, and one
re-sends a question with its layout changed to see whether the answer
holds. Both go to the platform the model id names, or to an endpoint you
set, a local llama.cpp included.
A request like this one looks fine and has three problems:
{
"model": "jev-latest",
"state": "My parcel came three days late and the lid is cracked.",
"questions": {
"team": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": { "billing": "Charges and refunds", "delivery": "Late or damaged parcels" }
},
"late": { "type": "noul", "instructions": "Did more than two parcels arrive late?" }
}
}
Finding
Why it matters
DEC001 on jev-latest
The alias moves with each release, so answers can change with no change on your side
DEC004 on team
A message about neither team is still forced into one of them
DEC102 on late
Jev does not count reliably. Ask about each parcel and count in code
Install
Where
What you get
Install
VS Code
Linting as you type, fixes on the lightbulb, and the Ask and Probe commands
The same program is an MCP server, so an
agent that writes Jev or OpenAI Decisions questions can lint what it wrote and
fix it before you see it. The tool descriptions and the server's instructions
name both vendors and all three request shapes, so an agent knows to call it
for any of them.
Editor
How
VS Code 1.101+
Nothing to install. The extension offers the server to the editor, which starts it when an agent calls a tool
Claude Code
claude mcp add decisionlint -- npx -y decisionlint-mcp
Cursor, Windsurf, anything else
Point it at npx -y decisionlint-mcp
It offers eight tools. lint_text lints a request or source code passed as
text and lint_paths lints files on disk, with each finding carrying the
edit that would mend it. fix_text returns the text with the safe fixes
applied and the report of what is left, and writes nothing. list_rules
lists every rule with what it means, and explain_rule returns a rule's own
page, with an example that is flagged and one that is not. Those send
nothing and need no key.
The other three are the editor's Ask commands and Probe
for an agent. ask_model asks Jev, or Luna with model: gpt-6-luna,
to check the questions in text or in files, and returns the lint report with
the model's findings added. plan_ask says what that would send, and sends
nothing. probe_question sends one question with its layout varied and
reports whether the answer held. The two that send use your key and cost
money, and their descriptions tell an agent to call them only when you ask.
The key is read from TYPESAFE_API_KEY or OPENAI_API_KEY in the
environment the client started the server with, or from .env.local or
.env in the directory it started it in, and never from a tool argument, so
no key lands in an agent's transcript. Every tool answers as structured
content beside the text, and none writes a file.
It is listed on the MCP registry as io.github.nolindnaidoo/decisionlint,
for a client that installs servers from there.
-y skips the install prompt on first run. To skip npx on every launch,
install it once with npm install -g decisionlint-mcp and set "command": "decisionlint-mcp" instead.
A project that already pins decisionlint for CI has the same server behind
npx decisionlint --mcp. The package with no flag is the one to give a client,
because decisionlint started bare is a linter that exits, not a server.
What it catches
Jev and Luna guarantee the type of the answer, not the answer. A badly formed
question still comes back with a confident-looking number. These rules catch
the mistakes that can be read from the text.
Code
Name
Default
What it means
DEC000
unreadable
hint
Part of the question is built at runtime, so the rules that need it did not run
DEC001
unpinned-model
warning
An alias such as jev-latest moves with each release, so answers can change with no change on your side
DEC002
choice-option-limit
error
A Choice has more than 255 options. The API rejects it
DEC003
score-level-limit
error
A Score has more than 10 levels. The API rejects it
DEC004
no-fallback-option
info
A Choice has no other or none of the above, so an input that fits no option is forced into one. The largest cost measured anywhere, and the hardest to tell from text
DEC005
duplicate
error
A question id, option or level appears twice. In an object the later one silently replaces the earlier
DEC006
criteria-shape
error
A Choice needs a map of options, a Score needs an array of levels, and a Noul takes true and false
DEC007
invalid-question
error
The type is missing or unknown, or the instructions are an empty string
DEC008
numeric-levels
warning
Score levels are bare numbers. Jev matches the state against each description and never sees its position
DEC009
too-few-options
info
A Choice with one option or a Score with one level gives every input the same answer
DEC010
unused-disable
warning
A decisionlint-disable comment silences nothing, so it can only hide a finding added later
DEC011
description-repeats-name
info
An option's description only repeats its name, or a Noul's criteria say yes and no. Jev matches the state against the description, and this one says nothing
DEC012
terse-instructions
info
The instructions are one or two words, so the question leans on its id. Jev is sent the instructions and never the id
DEC013
newer-model
info
model names a Jev version newer than the one these rules were measured on, so a finding may cost nothing there and a weakness new to it is not looked for
DEC014
question-limit
error
A request has more questions than the platform it goes to takes in one call. The platform rejects it
DEC101
double-negative
info
A question negates twice in one clause, or a negation sits directly on another
DEC102
arithmetic
warning
The question asks Jev to count or compare numbers
DEC103
date-comparison
info
The question asks Jev to order two times or measure the gap between them
DEC104
compound
off
A Noul joins two judgments with 'and'
DEC105
generation
warning
The question asks for a value or for text to be written
DEC107
negated-noul
off
A Noul with no criteria is phrased so that yes means something is absent
DEC108
inverted-criteria
off
A Noul's 'true' criterion describes the negative case
DEC110
degree-levels
off
Score levels are degree words, bare labels, or one word turned up and down. Nothing for Jev to match the state against
DEC111
numeric-encoding
off
The question refers to a value by hex or RGB encoding
DEC112
undefined-boundary
off
A Noul with no criteria turns on a word such as large, often or enough
DEC301
model-counting
warning
Jev reads the question as needing counting or arithmetic
DEC302
model-undefined-boundary
info
Jev reads a Noul as turning on a matter of degree with no stated line
DEC303
model-overlapping-options
info
Jev reads two options of a Choice as covering the same cases
DEC304
model-overlapping-levels
warning
Jev reads two levels of a Score as the same situation
DEC305
model-label-mismatch
warning
Jev reads an option as named for one thing and described as another
DEC306
model-criteria-off-topic
info
Jev reads the criteria as deciding something the instructions do not ask
DEC307
model-ordered-options
info
Jev reads a Choice's options as steps on one scale, which a Score would place between
DEC308
model-unordered-levels
info
Jev reads a Score's levels as unordered categories, which a Choice would pick from
DEC309
model-depends-on-sibling
info
Jev reads a question as needing another question's answer from the same request
DEC310
model-overlapping-questions
info
Jev reads two questions in one request as asking for the same judgment
DEC311
model-answer-not-in-state
info
Jev reads the state written in the file as not holding what the question asks about
DEC312
model-orders-in-state
info
Jev reads part of the state written in the file as giving orders to its reader
Each finding links to its own page, with an example that is flagged, one that is not, how to fix it and how to silence it. Each page links on to the TypeSafe documentation the rule comes from.
Where it looks
JSON and JSONC request bodies, in TypeSafe's shape or OpenAI's.
Object literals in JavaScript and TypeScript, including requests passed
inline to client.systemOne(...) and client.decisions.create(...), and
the Vercel AI SDK's decide({ questions }) for either vendor, with the
model id inside typeSafeAi.decisionModel('...') read as the model.
noul(), choice() and score() calls in files that use
@typesafe-ai/sdk.
Python: dicts, Noul(...), Choice(...) and Score(...) from
typesafe_sdk, from LangChain's langchain_typesafe with its
NoulCriteria(...), or from a library that wraps either, and requests
passed to system_one(...) or client.decisions.create(...).
Rust: the JSON inside json!, structs and enum variants named for a
question type, and ::noul(...) style constructors.
Go: maps with string keys, structs with a Type field, structs named
for a question type, and OpenAI's Go SDK, openai.DecisionNewParams{...}
with its OfPredicate, OfChoice and OfScore questions.
A request pasted as JSON into a string, in any of these languages.
Any other language: have your program write the request it sends to a
.jev.json file and open that. Every check runs on it, and nothing in it
is built at runtime, so nothing is skipped.
jev/ask rules in an oxlint-plugin-jev config.
OpenAI's Decisions API
OpenAI's Decisions API, model gpt-6-luna, asks the same kind of question
as Jev in a different shape. DecisionLint reads it from 0.5.0. What that
covers, exactly:
Read. A /v1/decisions request body, client.decisions.create(...)
in TypeScript and Python, and the Vercel AI SDK's decide(). A
predicate is read as a Noul, choices: [{ value, description }] as a
Choice's options and levels: [{ label, description }] as a Score's
levels, into the same rules.
Checked. Every rule with evidence behind it: the missing fallback
option, a description that only repeats its name, terse instructions,
degree levels, duplicates, counting, an undefined boundary and the rest.
Findings name Luna. OpenAI's own guide asks for the same things.
Held back.DEC002 and DEC003 check a platform's published
limits, and OpenAI publishes none, so a request bound for OpenAI gets
neither. One bound for a gateway that does, such as Vercel's, gets both.
DEC001 is about Jev's moving aliases, and OpenAI has one model id.
Shape mistakes are reported in OpenAI's own field names: choices
written as a map or as bare names, a Choice with no choices, a Score
with no levels. A mistyped predicate is fixed on the lightbulb. The
reshape fix is Jev-only.
Asking Luna. Run Ask Luna About This File, or pass
--ask-platform openai, with an OpenAI key. The next section says how.
Calibrated on Jev, with cutoffs of its own on Luna. Every default in
the wording rules was set by measuring what the defect costs jev-1.13.0.
The model-backed checks were run on Luna over the whole calibration set on
2026-10-24 and each got a cutoff fit on Luna's own answers, where one
could be: seven of the twelve. The other five are not asked of Luna,
because no cutoff told the examples with the defect from the examples
without, and docs/cutoffs.md names the example in the
way of each. What the wording rules cost Luna is not measured yet.
Every platform
Jev and Luna are two of the models, and TypeSafe and OpenAI two of the
places a request can go. Microsoft, Liquid AI and Cloudflare serve their own
models in TypeSafe's shape. OpenRouter, Vercel AI Gateway, LiteLLM and LLM
Gateway forward to any of them, in either shape. llama.cpp, vLLM and Ollaya
run open models on your own machine. DecisionLint reads the two shapes, so
every one of them is covered by the same rules. What differs between them
is held in one table, src/lint/targets.ts, with the page each fact was
read from:
Platform
Reached as
Key variable
Limits it publishes
TypeSafe
jev-1.13.0, jev-latest
TYPESAFE_API_KEY
255 options, 10 levels
OpenAI
gpt-6-luna
OPENAI_API_KEY
none
OpenRouter
~typesafe/jev-latest, typesafe/jev-1.13
OPENROUTER_API_KEY
none
Vercel AI Gateway
typesafe-ai/jev, openai/gpt-6-luna-decisions
AI_GATEWAY_API_KEY
255 choices, 10 levels
LLM Gateway
gpt-6-luna-decisions, microsoft-decision-1
LLM_GATEWAY_API_KEY
none
Liquid AI
d1, d1:free
LIQUID_API_KEY
none
LiteLLM
an endpoint you set
the shape's
128 questions per request
Eden AI
its own path
its own
255 options
llama.cpp
an endpoint you set
none
10 levels
Three things follow from the table:
The limits a request is held to are its platform's. The platform is
read from the model id where its spelling says, from the one platform a
URL in the file names, or from target in decisionlint.json or
decisionlint.target for a project that sends everything to one place. A
platform that publishes no limit gets no finding for it. Nothing says
where a request goes: a TypeSafe-shaped question keeps TypeSafe's limits,
and any other shape keeps none.
A floating alias is caught in every spelling, and pinned in the
spelling the platform documents: jev-latest to jev-1.13.0,
~typesafe/jev-latest to typesafe/jev-1.13.
A check with the model goes to the platform the id names, with the key
in that platform's variable. A spelling that names no host, such as
openai/gpt-6-luna-decisions, which every gateway writes, is refused
rather than guessed at, until you set the endpoint. The next two sections
say how.
"Open Jev" is not one of these. It is a name used by at least nine
unrelated projects that reimplement /v1/systemone; the one llama.cpp
serves is ggml-org/OpenJev-GGUF. Each is reached as an endpoint you set.
Where it has been tested
Every platform below was sent real requests through its live API, and the
model-backed checks were run over the whole calibration set on each:
TypeSafe, jev-1.13.0, where Jev's cutoffs were set.
OpenRouter, typesafe/jev-1.13. It answers as TypeSafe does, so its
findings are treated as calibrated.
LLM Gateway, jev-1.13.0, and Vercel AI Gateway, typesafe-ai/jev. Both
are sent OpenAI's shape, which moves Jev's answers a little, so their
findings say the cutoff was set at TypeSafe.
OpenAI, gpt-6-luna, where Luna's cutoffs were fit, and Vercel AI
Gateway, openai/gpt-6-luna-decisions, which answers as OpenAI does.
Liquid AI, d1, where d1's cutoffs were fit.
Luna and d1 have cutoffs of their own since 1.1.0, each fit on that model's
run over the whole calibration set: a cutoff flags none of the good examples
and catches at least six in ten of the bad ones. Where no cutoff can, the
check is not asked of that model: five checks on Luna and two on d1. Any
other model, or one of these under another id or through another route, is
judged by its family's cutoffs, or Jev's, and every finding from it says so.
docs/cutoffs.md has each cutoff, how many known
defects it caught and how many good questions it flagged on each model, why
each check that is not asked is not, and how far each platform's answers sit
from the same model elsewhere. It is written from the saved runs, so its
numbers are the runs' own. LiteLLM, Eden AI and llama.cpp have not been run
yet.
Ask a model about a file
Rules DEC301 to DEC312 are not part of linting. They run only when you run
one of the Ask commands, or pass --ask on the command line. Each sends
every question in the file to a decision model and asks it about problems a
text pattern cannot see: options that overlap, an option whose name
contradicts its description, criteria about the wrong thing, a Choice that
should be a Score, two questions that ask the same thing, a question that
needs another question's answer.
There is one command per platform, and each has its own key:
Command
Sends to
Model by default
Key
Ask Jev About This File
TypeSafe
jev-1.13.0
Set TypeSafe API Key, or TYPESAFE_API_KEY
Ask Luna About This File
OpenAI
gpt-6-luna
Set OpenAI API Key, or OPENAI_API_KEY
Ask via OpenRouter About This File
OpenRouter
typesafe/jev-1.13
Set OpenRouter API Key, or OPENROUTER_API_KEY
Ask via Vercel AI Gateway About This File
Vercel AI Gateway
typesafe-ai/jev
Set Vercel AI Gateway API Key, or AI_GATEWAY_API_KEY
Ask via LLM Gateway About This File
LLM Gateway
jev-1.13.0
Set LLM Gateway API Key, or LLM_GATEWAY_API_KEY
Ask Liquid d1 About This File
Liquid AI
d1
Set Liquid AI API Key, or LIQUID_API_KEY
Ask the Model at My Endpoint About This File
decisionlint.ask.endpoint
decisionlint.ask.endpointModel
Set Endpoint API Key, or none
Give it the platform's key, in any of three ways. Run its Set ... API
Key command and paste the key, which stores it in your operating system
keychain. Or type it into Settings under decisionlint.ask.keys,
by the platform's id, which can only be set in your user settings and is
left out of Settings Sync. Or put it in the environment variable the
platform names, in the environment VS Code runs in.
Open a file with questions and run the platform's Ask command.
The model each command sends is decisionlint.ask.models, by platform.
The TypeSafe default is the version the checks' cutoffs were set on, and a
finding from any other model, Luna included, says so. OpenRouter's
typesafe/jev-1.13 was measured to answer as that version does, so its
findings do not. The probe asks Jev at TypeSafe, with the TypeSafe
key and model.
For a platform you host, a llama.cpp, vLLM, LiteLLM or Ollaya, set
decisionlint.ask.endpoint to the full URL it listens at,
decisionlint.ask.endpointModel to the model id it serves, and
decisionlint.ask.endpointShape to typesafe or openai when the path
does not end in /systemone or /decisions. Then run Ask the Model at My
Endpoint. It sends the endpoint key if you set one, else the key in
TYPESAFE_API_KEY or OPENAI_API_KEY, whichever the shape's SDK reads, or
no key when there is none, which is how a local llama.cpp is reached. The
endpoint can only be set in your user settings, so a repository cannot point
your key at a host of its choosing.
What to know before you run it:
It uses your key and your credits. One request per question and one more
per request body, roughly 800 input tokens each on Jev and twice that on
Luna. At TypeSafe's published price that is a few thousandths of a cent
per question, and at OpenAI's one or two hundredths. The count shown before
sending is an estimate, rounded up, and what the platform bills can differ.
Only questions are sent: type, instructions, criteria and ids. Your
state is not, unless you turn on decisionlint.ask.sendState. With that on,
a state written out in the file is sent too, and two more checks run: does
the state hold what each question asks about, and does it carry text that
gives orders to its reader.
A question with a part built at runtime is not sent. The summary says
how many were held back.
A run sends at most decisionlint.ask.maxCalls requests, 25 by default.
It is disabled in an untrusted workspace. Run Workspaces: Manage
Workspace Trust and trust the folder to turn it on.
Findings disappear when you edit the file, because they were about the
old text. Editing or closing the file during a check stops the check.
Each finding shows the probability Jev gave it. Treat it as an argument
with a number attached, not a verdict.
Turn on decisionlint.ask.confirm and it first tells you how many requests it
will make, roughly how many tokens and what that costs, and waits for a yes.
It is off by default: running the command is the decision to send.
Probe a question
Put the cursor in a question and run DecisionLint: Probe the Jev Question at
the Cursor. It sends that question against the state written beside it,
three times as written and then with the layout changed: options reversed,
option names hidden, levels reversed, criteria removed. A report opens showing
whether the answer moved.
If the answer changes when only the order of the options changes, the
question is not deciding it. If three identical requests disagree, the input
is too close to call.
It needs the state written out in the file, it sends that state, and it costs
five or six requests.
Nothing else in this extension uses the network. On the command line, only
--ask does.
Project settings
Put a decisionlint.json in your project and the editor and the command line
both read it, so what you see while writing is what CI reports.
exclude lists files and folders not to lint, relative to the settings file.
** crosses folders, * stays inside one, and a name with no slash matches
at any depth. The editor and the command line leave out the same files.
The nearest file wins, looking from the linted file upward, so a folder can
have its own. Where one applies, it replaces the rules, fallbackOptions
and ignore settings in the editor. If it cannot be read, nothing is linted
and the status bar says why. A key it does not know is an error, so a typo
cannot quietly leave a rule on.
To silence one finding, use the lightbulb: Disable DEC004 for this line
or for this file writes the comment for you. Strict JSON has no comments,
so there a finding is silenced with ignore.
What it will not tell you
Whether a well-formed question is right on your data. That needs labeled
examples and calls to Jev.
Anything about a value it cannot see. A question built from variables is
reported as unreadable and counted in the status bar. A file is never shown
as clean while part of it went unread.
Everything about wording.DEC101 to DEC112 are heuristics over
English text. A rule is on only where a badly written question cost answers
or confidence against Jev, in our runs or in someone else's, and it stays
on only while it is right on at least 9 of 10 of its findings on public
code it was not tuned against. Four that describe failure modes in
TypeSafe's docs, DEC104, DEC107, DEC108 and DEC111, did not fail on
jev-1.13.0 and are off. DEC110 and DEC112 cost answers but fell under
9 of 10 on the second public sample, at 8 of 9 and 2 of 3, and are off
since 1.1.0. Switch any of them on in decisionlint.rules. Two more were
removed in 0.4.0 for being wrong every time they fired.
Anything about a question that is not in English.
What a lone object lacks. A { type, instructions } outside a
questions map may be a template or half of a builder, so a missing
criteria is only reported inside a request.
DEC004 is informational because the advice behind it is conditional: add a
fallback when the options might not cover every input. It fires on 9 of the 11
Choice examples in TypeSafe's own docs. If your options are exhaustive, turn it
off for that question.
decisionlint-disable-line covers the same line and decisionlint-disable the whole file.
Leave the code off to silence every rule.
A comment that silences nothing is reported as DEC010, so one left behind
after its finding was fixed cannot hide a new finding later.
What was written for JevLint-LE still works: a jevlint-le-disable comment, and the JEV codes in a comment, in rules or in ignore, are read for ever. Every finding is written with the DEC code.
Fixing
Some findings can be mended without a decision from you: a Noul criteria key
written yes where the API takes true, a mistyped question type, criteria
in the wrong shape. npx decisionlint --fix writes those, and the summary of
any run says how many it would mend.
Adding a fallback option and pinning a model change what a working request
does, so they are never written for you. They stay on the lightbulb.
JSON has no comments, so use the setting:
{ "decisionlint.ignore": ["DEC004:side"] }
The CLI
The same linting runs outside the editor, for CI and for any editor that is
not VS Code.
One version for the editor and CI
Install it in the project, as you would any linter:
npm install --save-dev decisionlint
The editor then lints with that copy, not the one the extension carries, so
what you see while typing is what npx decisionlint reports in CI. The version
changes when package.json does, and for everyone at once. The status bar
shows project 0.5.2 while a project's copy is in use.
With nothing installed the extension lints with its own copy, with no setup.
It also uses its own copy, and the status bar says project has 0.2.0 with
the reason in its tooltip, when the project's copy cannot be used:
the workspace is not trusted, because loading the copy runs code from it
the copy is older than 0.3.0, the first version an editor can load
the copy fails to load, or does not read that kind of file
When the copy's package.json cannot be read there is no version to show, so
the status bar says project copy unreadable, again with the reason in its
tooltip.
The Ask commands and the probe always run the extension's own
code, whatever the project installs.
npx decisionlint src/ # a directory, searched for files it reads
npx decisionlint request.jev.json # one file
npx decisionlint --format github . # annotations on a pull request
It exits 0 when the run passes, 1 when a finding fails it, and 2 when it
could not do what was asked: an unknown option, a missing path, or nothing to
read. Errors always fail a run. Warnings fail it only past --max-warnings.
How findings are printed. stylish groups them by file and is the default. compact is one per line as path:line:column. sarif is for code scanning and junit for test reporters. Also -f
--rule <CODE=level>
Set one rule to off, hint, info, warning or error. A rule's name works in place of its code, and warn means warning
--config <file>
Use this settings file, and no decisionlint.json found near the files. Also -c
--max-warnings <n>
Fail when more than n warnings are reported
--stdin-filename <path>
Lint standard input as if it were that file
--fix
Write the fixes that only mend what the API would refuse
--quiet
Print errors only
--ask
Also ask the model about each question. Sends them to the platform the model id names: TypeSafe with TYPESAFE_API_KEY by default, OpenAI with OPENAI_API_KEY under --ask-model gpt-6-luna, OpenRouter, Vercel AI Gateway, LLM Gateway or Liquid AI under their spellings, or wherever --ask-endpoint says
--ask-plan
Say what --ask would send, and send nothing
--ask-platform <id>
Where --ask sends, named outright: typesafe, openai, openrouter, vercel, llmgateway or liquid, each with its own key variable and a default model in its own spelling. Without it a model id goes where its spelling says
--ask-model <id>
The model --ask asks: a Jev version, gpt-6-luna for OpenAI's Decisions API, or a model id in another platform's spelling. Default jev-1.13.0
--ask-endpoint <url>
The full URL --ask posts to, for a platform you host or a gateway spelling of the model that names no host. Also read from DECISIONLINT_ASK_ENDPOINT. A host the tool does not know is sent the key of the shape, or none
--ask-shape <shape>
The shape sent to --ask-endpoint where its path does not end in /systemone or /decisions: typesafe or openai. Also read from DECISIONLINT_ASK_SHAPE
--ask-max-calls <n>
The most requests --ask may send in the run. Default 25
--ask-send-state
With --ask, also send state written out in a file
--color, --no-color
Colour the default format, or do not. Without either it is coloured in a terminal, unless NO_COLOR is set
--no-error-on-unmatched-pattern
Pass when the paths hold no file to lint
--mcp
Run as an MCP server
The summary line always says how many questions could not be read in full
and names any file skipped for its size and any folder or file it could not
read. It does not follow linked folders. It does not run the checks that ask
Jev, and it never uses the network, unless you pass --ask.
--format junit writes the XML that Jenkins, GitLab and most test reporters
read. Only an error is a failed case there, since only an error fails a run.
--ask runs the checks that ask a decision model, DEC301 to DEC312, after
the linting. It is the only option that uses the network, and nothing in a
settings file can turn it on.
npx decisionlint --ask-plan src/ # what it would send. No key, nothing sent
TYPESAFE_API_KEY=... npx decisionlint --ask src/ # send it
--ask-platform names where it goes: typesafe, openai, openrouter,
vercel, llmgateway or liquid, each with a default model in its own
spelling and its own key variable, TYPESAFE_API_KEY, OPENAI_API_KEY,
OPENROUTER_API_KEY, AI_GATEWAY_API_KEY, LLM_GATEWAY_API_KEY or
LIQUID_API_KEY. Without it, --ask-model with a platform's spelling,
~typesafe/jev-latest for OpenRouter, sends there, and a bare id goes to
its vendor. The key is read from that variable and from nowhere else, and
is never printed.
--ask-endpoint, or DECISIONLINT_ASK_ENDPOINT, sends to a platform you
host, with --ask-shape where the path does not say which shape it takes.
A spelling that names no host, such as openai/gpt-6-luna-decisions, is
refused without it, with nothing sent.
A question with a part built at runtime is never sent. The summary counts
the ones held back.
State is not sent unless you add --ask-send-state.
--ask-max-calls is a limit for the whole run. A run that needs more exits
2, so a job cannot pass after checking part of a project.
A rejected key, a failed request or a run you stop also exits 2, and says
how many requests were answered of how many were planned.
These findings are warnings or less by default, so they fail a run only
when --rule raises one or --max-warnings is passed.
Commands
Every command can be given a shortcut. Open Keyboard Shortcuts, search for
DecisionLint, and press the keys you want on any command in the list. None is
bound out of the box, so nothing here can collide with a shortcut you already
use.
Or write them into keybindings.json. The keys here are only an example:
The Ask commands and the probe send requests with your key, so pick keys for
them you will not press by accident.
Settings
Setting
Default
Meaning
decisionlint.rules
{}
Severity per rule, by code or name: off, hint, info, warning, error. An entry that names no rule or no level stops linting and says why, as it would in a decisionlint.json
decisionlint.fallbackOptions
other, none, unknown, unclear, uncertain, unsure, ambiguous, undetermined, neither, not stated, not applicable, insufficient evidence, cannot tell, n/a, and "other" in five other languages
Words that make an option a fallback for DEC004. An option counts when one of them is a word in its name, so none_implied and other_or_unclear count
decisionlint.ignore
[]
CODE:questionId entries to drop
decisionlint.target
""
The platform every request goes to, for its limits. Empty reads it from each model id
decisionlint.ask.models
jev-1.13.0 for typesafe, each platform's own default for the rest
The model id each Ask command sends, by platform. The TypeSafe default is the version the checks were calibrated on
decisionlint.ask.keys
empty
An API key per platform, and endpoint for a host you name, if you would rather type them here than use the keychain. User settings only, not synced
decisionlint.ask.endpoint
""
The URL Ask the Model at My Endpoint posts to, for a platform you host. User settings only
decisionlint.ask.endpointShape
""
The shape sent to that endpoint, typesafe or openai, where its path does not say
decisionlint.ask.endpointModel
""
The model id sent to that endpoint
decisionlint.ask.maxCalls
25
The most requests one Ask command may send
decisionlint.ask.sendState
false
Also send a state written in the file, so the model can check it
decisionlint.ask.confirm
false
Before a paid command sends anything, show what it will send and cost, and wait for a yes
decisionlint.include
JSON, JavaScript, TypeScript, Python, Rust and Go files
Files the workspace command lints
decisionlint.exclude
node_modules and build output
Files the workspace command skips
decisionlint.maxFileSizeBytes
1000000
Larger files are reported as skipped
decisionlint.notificationsLevel
important
How much is said in notifications. all adds a summary after each command, and silent shows a message only when a command could not run. Findings always show in the editor
Privacy & security
Linting never uses the network. The extension, the command line and the
MCP server lint with no key and send nothing. No telemetry is collected.
Only the Ask commands, the probe, --ask and the two MCP tools that send
reach a platform, with your key, and only when you run them. They send
questions, not your state, unless you turn on decisionlint.ask.sendState
or --ask-send-state. The probe sends the state written beside the question.
Keys stay out of the repository. The Set ... API Key commands store a
key in your operating system keychain. decisionlint.ask.keys and
decisionlint.ask.endpoint can only be set in your user settings, which are
left out of Settings Sync, so a repository cannot point your key at a host of
its choosing. The command line reads a key from its environment variable
only and never prints it.
Untrusted workspaces cannot send. The Ask commands are disabled there, and
a project's own copy of the linter is not loaded, because loading it runs
code from the workspace.
The MCP server writes nothing, and reads a key from the environment the
client started it with, or .env.local or .env in its directory, never
from a tool argument, so no key lands in an agent's transcript.
bun install
bun run typecheck && bun run lint && bun run test
bun run build
bun run test:integration # real VS Code against samples/
bun run package # release/decisionlint-<version>.vsix
bun run test:e2e-vsix # the packaged VSIX in a clean profile
AGENTS.md is the engineering standard. SPEC.md is what the product is and
what is planned. samples/ is a workspace with planted mistakes, used by
bun run test:integration, which drives a real VS Code.
More from the LE family
Sixteen single-purpose tools for the work in front of every model. Each ships
a Rust CLI and an MCP server. One page: letools.dev
Get it out
String-LE — Extract every string in a codebase, so a person can read them
Numbers-LE — Extract every hardcoded number in a codebase, so a person can check them
Units-LE — Extract every quantity with its unit, normalized, and refuse the ambiguous ones by name
Dates-LE — Extract every date and timestamp, and the exact instant each one resolves to
IDs-LE — Extract every UUID, ULID, NanoID, ObjectId and Snowflake, and decode the time inside
IPs-LE — Extract every IP address, CIDR block and MAC, normalized and classified by scope
URLs-LE — Extract every URL in a codebase, with its protocol and exact position
Paths-LE — Extract every file path in a codebase, and say whether it still points at anything
Colors-LE — Extract every color in a codebase, and say which ones are not in your palette
Check it
Regex-LE — Find every regex in a codebase, and report which can be driven into catastrophic backtracking
Versions-LE — Find where one dependency is constrained differently across a repository's manifests
i18n-LE — Identify the i18n library a project uses, then audit its catalogs by that library's rules
Scrape-LE — Check whether a page is scrapeable before the scraper is written, and say when it cannot tell
Guard it
Secrets-LE — Find hardcoded credentials in a codebase, and never print one into the report
EnvSync-LE — Compare the dotenv files in a tree, and say which keys are missing from which
Unicode-LE — Find the Unicode that hides meaning — bidi controls, invisibles, homoglyphs, mixed scripts
Each stands on its own: no shared crate, no published core. Where two of them
agree, it is because the same answer was right twice.
Rust — pixelcoords and pixelactions are one loop: pixelcoords answers
where, pixelactions acts there. Their own tools, their own voice — not
part of the LE family.
Chưa có danh sách công cụ. Máy chủ từ xa sẽ có danh sách sau lần kiểm tra tình trạng đầu tiên.
Câu hỏi thường gặp
Câu hỏi
Làm thế nào để cài đặt DecisionLint: Jev/Luna Lint?
Sao chép cấu hình stdio cho Cursor, Claude Desktop, Claude Code hoặc VS Code trên trang này. Cấu hình dùng bản cài đặt của nhà phát hành, không phải proxy của PlugMCP.
DecisionLint: Jev/Luna Lint có cần khóa API không?
Bản ghi này không liệt kê biến môi trường bắt buộc nào.
DecisionLint: Jev/Luna Lint dùng giao thức truyền tải nào?
DecisionLint: Jev/Luna Lint chạy cục bộ qua stdio.
DecisionLint: Jev/Luna Lint được kiểm tra lần cuối khi nào?
Kiểm tra lần cuối: 2026-11-10. Lint Jev and Luna decision-model questions, or ask the model to check them with your key.
