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Choose a template or define the system role, task, context, constraints, output format, and examples
Choose a template or define the system role, task, context, constraints, output format, and examples.
Flagship use case · Developer, Data & AI
Prompt & Context Workbench
Structure LLM prompts, estimate context usage, detect common instruction risks, define evaluation cases, and version prompt snapshots locally.
App v1.0.0Site v6.2.0Local by default
Interface preview
Working application structure
This representative capture shows the application shell and primary workspace. Actual results depend on the information entered and the workflow completed.
Desktop viewport · representative interface stateOpen live application →
Visible methodAssumptions and record logic remain reviewable.
Local-first postureData handling is stated in the application.
Portable outputExports support review and continuity.
Explicit boundariesLimitations are part of the product evidence.
User and problem
Where the tool fits
Designed for: People designing reusable prompts, context packages, output requirements, and evaluation cases for language-model workflows.
Problem addressed: Prompts become unreliable when instructions, source context, constraints, output formats, examples, and evaluation criteria are mixed together without structure.
Core workflow
From input to decision-ready output
Frame→Structure→Analyze→Export
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Configure the context-window and output-reserve assumptions, then review static checks and estimated usage
Configure the context-window and output-reserve assumptions, then review static checks and estimated usage.
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Create evaluation cases, save snapshots, compare revisions, and export Markdown or JSON
Create evaluation cases, save snapshots, compare revisions, and export Markdown or JSON.
Method
Transparent calculation and record logic
The methodology is intentionally visible so users can challenge the assumptions and validate the result against authoritative evidence.
How the application works
- Compiles labeled prompt sections in a consistent order.
- Estimates tokens using a configurable character-to-token heuristic and reserves output capacity before calculating utilization.
- Runs static checks for missing task definition, weak boundaries, conflicting instructions, and absent output requirements.
Illustrative output
Prompt estimate: 2,460 tokens Available after reserve: 124,000 Context utilization: 2.0% Quality check: 92 / 100
Boundaries
What the result does not prove
- Token counts are heuristic and vary by model tokenizer and content.
- Static checks cannot guarantee model behavior, factual accuracy, safety, or instruction priority.
- No model API is called; evaluation cases are planning records rather than executed tests.
Working data remains local to this browser unless the application explicitly describes an external request or the user exports a file. Browser storage can be lost when data is cleared or the device changes.