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Tell AI Once.

Taskless creates constraints for your agent harness. You tell Taskless what you want, and Taskless figures out how to enforce it: on every commit, every PR, in every IDE, on every run.

Every rule compiles to a deterministic check with its own tests. No model in the loop when it runs.

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No signup required to generate rules locally.

agent
AGENTS.md Rules vs. Taskless Rules across 4 runs
RunInputAGENTS.md RulesTaskless Rules
Run 1Agent ships export function parse(input: any)Missed: Use stricter types hereMissed: Use stricter types here
Run 2You: “@taskless no any in exported types”Noted: One more line in AGENTS.mdRule created: no-any-in-exports (2 fixtures · pass / fail)
Run 3Agent writes as unknown as anyPassed: Looks fine to meBlocked: Disallowed: any in exported type
Run 4Model update. Agent tries T extends anyPassed: No issues foundClean: Fixed before commit. Same rule, same result.

Markdown Rules
Don’t Work

AGENTS.md, skills/, .cursor/rules. They all inject markdown into the AI’s prompt and hope it listens. Different answer every time.

Taskless writes a rule and hands it to something that can actually decide it. Real analysis, not prompt engineering. Same result, every run.

Code review bots guess. Taskless knows.

A Constraint for
Every Convention

You tell Taskless what you want. Taskless picks whatever can decide it: a syntax tree, the prose itself, your own linter, or a check that reads across files.

25+ languages and the prose you write, locally, with no account. A rule your linter can express goes into that tool’s own dialect, so your lint step runs it. When the answer is more complex, Taskless Cloud steps in and writes secure, testable code.

Deterministic By Design

Not AI opinions. Not prompt engineering. Rules that produce the same result every time.

Rule fixtures pass and fail the same way on every run

Every Rule Proves Itself

Every rule ships with its own pass/fail fixtures, so it proves it catches what you meant before it touches your code. Same result on the first run and the thousandth.

Code review rules sourced from team decisions

Your Decisions, Not AI Opinions

Rules come from your corrections, not generic training data. Every rule traces to something your team actually decided.

Single source of truth replacing scattered markdown rule files

No Files to Maintain

No AGENTS.md to keep current. No skills/ to curate. No .cursor/rules to sync. Correct a mistake and move on.

Each correction compounds into the next pull request

Compounds Over Time

Every correction becomes a rule. Every rule makes the next PR cleaner. Your codebase gets better without meetings about it.

Do More With
Taskless Cloud

The free, open-source CLI runs locally with no account. Taskless Cloud is for when the concern is bigger than one team.

ProvenanceEvery rule traces to the PR or decision it came from.
GovernanceOne rule holds for every developer and every agent in the org.
AuthenticityRule code is signed and can’t be tampered with on the way to the check.

Also in Cloud: runtime rules and GitHub integration.

Install in One Command

Detects Claude Code, Cursor, and OpenCode. Works with Codex and anything else that supports Agent Skills. No account required to start.

Frequently Asked Questions

Is Taskless free?
Yes. The CLI is open source and MIT licensed, runs locally, and needs no account. Taskless Cloud is free for individual developers and small teams, with usage caps.
Is this another AI code reviewer?
No. AI code reviewers are LLMs. They give different feedback every run because that’s how language models work. Taskless creates deterministic rules from your corrections. Same input, same result, every time. It’s not reviewing your code with AI. It’s enforcing your team’s decisions with real analysis.
How is this different from AGENTS.md or Cursor Rules?
AGENTS.md, .cursor/rules, the skills/ directory, and similar standards all work the same way. Markdown gets injected into the AI’s prompt. The AI might follow it. Or it might not, especially in long conversations, complex tasks, or after model updates. Taskless creates rules that are analyzed rather than interpreted: a syntax tree for code, the words themselves for prose. They’re not suggestions to an LLM. They’re deterministic checks that pass or fail the same way every time. Think of it as the difference between asking someone to remember a rule and writing an automated test for it.
How does Taskless learn from my corrections?
Tag @taskless on a pull request or issue, use the dashboard, or create rules directly from your IDE with Agent Skills. Taskless routes your correction to whatever can decide it: a syntax tree, the prose itself, your own linter, or a check that reads across files, whichever fits the problem.
What if a rule is wrong or needs updating?
You own your rules. Ask Taskless to revise them, or update them yourself with your AI coding agent.
Do I need to change how my team works?
No. Once the first rule is in place, Taskless runs in the background like any other CI step. Nothing new to learn, nothing to context-switch into.
Does Taskless see my code?
Only when you ask it to. Tagging @taskless on a PR is an explicit request. Rules are generated using coding agents with guardrails built, recursively, with Taskless itself. Every rule includes fixtures so you can see exactly what’s being checked.

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Why we built this

We built Taskless after watching our own coding agents ignore our own AGENTS.md, no matter how many times we rewrote it. We run Taskless on our own code and our own writing, including the docs on this site.