Build prompts that fit the way you actually work.
openPrompting combines model-specific prompting guidance, coding-tool context and behavior, and task-specific structure into a prompt that fits your setup.
Choose the AI model you’re using, the tool you’re working in, and what you’re trying to do. openPrompting brings the relevant guidance together — without calling another AI.
No API key · No model calls · No backend
One prompt doesn’t fit every setup.
Different AI models respond differently to instructions. Coding tools manage context and project rules in different ways. And planning a feature needs a different structure from fixing a bug.
openPrompting combines all three into guidance for the setup you’re actually using.
Guidance for this setup
GPT-6 Astra · Codex · PlanningModel — GPT-6 Astra
Model guidance- State the target outcome, acceptance criteria, constraints, and relevant context.
- Specify required output structure and genuine invariants.
- Define when to ask for missing evidence, when a reasonable assumption is allowed, and when the task is complete.
Harness — Codex
Harness behavior- Put durable repository instructions in a scoped
AGENTS.md. - Keep project instructions concrete, current, and compatible with repository tooling.
- Include actual validation commands and boundaries the agent must preserve.
Task — Planning
Task structure- Inspect the current system, relevant files, configuration, tests, and constraints before prescribing a change.
- Separate known requirements and observed facts from assumptions, decisions, and open questions.
- Identify the smallest sound implementation path, its dependencies, owners or handoffs, and the points where the plan should be revisited.
Selections above are choices, not rankings. openPrompting does not rank models or imply that any combination is objectively superior. Profiles are user-defined.
A browsable index of the current knowledge base.
Every entry is a versioned document in the repository with structured evidence and a verification date. New entries appear here automatically at build time.
What a knowledge entry actually contains.
Pulled directly from knowledge/harnesses/cursor.md at build time — not written for this page.
Cursor
Cursor is an editor and CLI coding-agent harness. These notes cover its rules, context, and permission behavior rather than the underlying model.
Recommended
- Put simple project-wide instructions in
AGENTS.md; use.cursor/rules/*.mdcwhen the guidance needs file scoping, intelligent attachment, or manual invocation. - Use the
.mdcextension and explicit rule frontmatter (description,globs, andalwaysApply) for project rules; keep rules focused and version-controlled. - Use nested
AGENTS.mdfiles or scoped rules for genuinely local conventions, and review the resulting instruction set when a task crosses directories. - Treat CLI shell and file-read permissions as part of the setup. Grant only the command and path access the workflow needs, especially before using non-interactive mode.
Evidence
- officialCursor project rules are version-controlled .mdc files under .cursor/rules and can be applied always, by file pattern, intelligently, or manually.
Rules ↗ - officialCursor supports AGENTS.md project instructions, including more specific instructions in nested directories.
Rules ↗ - officialCursor CLI uses the IDE rules system and reads AGENTS.md and CLAUDE.md at the project root when present.
Using Agent in CLI ↗ - officialCursor CLI permissions can separately control shell commands and file reads through global or project configuration.
Permissions ↗
Normal operation touches nothing outside your machine.
openPrompting resolves guidance from a local knowledge base. After installation it needs no account, no API key, no model call, no hosted backend, and no network connection. Setup is the only command that writes project state; doctor is read-only.
- No account
- No API key
- No model call
- No hosted backend
- No telemetry
- No runtime network
Every claim has a source and a class.
Each knowledge entry carries structured evidence metadata: what class of evidence supports a claim, which source it came from, and when that source was last checked. Every entry page surfaces that metadata.
Official
Supported directly by first-party provider or harness documentation.
Tested
Reproduced by maintainers with a procedure recorded in the entry.
Community
Useful practice without first-party confirmation.
Legacy
Historical or migration advice, not treated as the current default.
Six commands. Nothing hidden.
The command line configures, resolves, generates, diagnoses, and compares. It never calls a model — it reads the same knowledge you are browsing here.
helpsetupguidenewdoctorcompareFull selectors, resolution rules, and exit behavior: Docs →
From install to a real task prompt in four steps.
- 01Install
npm install --global openprompting - 02Configure
openprompting setup - 03Read guidance
openprompting guide - 04Start a task
openprompting new planning
Agentic coding, in one paragraph.
A model reasons and generates; a harness gives it repository access, tools, and instruction files. Together they form a setup, which you can name as a profile. Good prompting is rarely magic words — it is giving the agent goals, context, requirements, constraints, acceptance criteria, and validation.