Stop loading instructions
you don't need.
Know what your agents are loading, why they are loading it, what it costs, and whether it can be packaged better. tokencheat audits instruction architecture, extracts reusable workflows into skills, and reduces unnecessary context without blindly removing capability.
The workspace the founding cohort gets.
Saved audit history, context scores over time, and a compiler that turns findings into conservative optimizations. Explore the screens — this is a live preview with illustrative data, not the product API.
Saved audits
24
Average context score
71
Best score
94
Est. monthly savings
$412
claude-code · CLAUDE.md
Aug 18
cursor rules · monorepo
Aug 16
support-agent system prompt
Aug 14
mcp servers · 6 enabled
Aug 12
review-bot instructions
Aug 09
Start measuring in the next sixty seconds.
Every tool runs free in your browser — no signup, and pasted text never leaves your machine. Every number carries its source.
Audit
Team favoriteAgent Config Audit
Score and conservatively optimize always-on agent instruction files in the browser.
Open tool →
Measure
Team favoriteAI Token Calculator
Estimate LLM cost from input, output, and cache tokens using maintained per-million pricing. Estimates only — verify against your provider invoice.
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Measure
Team favoriteModel Price Comparison
Provider-by-provider view of per-million input and output rates with context windows, sourced from provider pricing pages.
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Optimize
Skill Compiler
Start with Prompt → Skill today; inspect the full roadmap for repo audits, structural builds, host packages, provenance, and behavioral validation.
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Optimize
Instruction Stack Optimizer
Audit pasted instructions with the full linter and quality index, then compile a layered policy pack — compact trigger profile plus Claude Code, Cursor, and Copilot exports.
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Measure
MCP Context Estimator
Estimate standing tool-schema context and repeated context exposure from enabled MCP servers.
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Don't ask only how to make the prompt shorter.
Ask which instructions must be active now and which only need to be discoverable later. The cheapest instruction is the one loaded only when needed, in the smallest form that preserves required behavior.
A build pipeline for agent instructions.
Measurement comes first. Safe transformation and behavioral evidence come before distribution.
Collect. Pull the instruction files, skills, and MCP configs your agents actually load — the real always-on surface, not the intended one.
Separate the framework from the expertise.
Runtime
Session setup, state, host behavior, checkpoints, artifact paths and operating mechanics. Repeated runtime is a strong structural optimization target when evidence is clear.
Policy
Cross-cutting safety, evidence, completeness, interaction and output rules. Share or defer policy only when required behavior remains explicit.
Capability
The domain methodology that makes a skill valuable. tokencheat protects capability aggressively instead of treating every long instruction as waste.
Measure, audit, optimize, validate.
Not every optimization deserves the same trust.
A compiler people can inspect.
Every recommendation should include evidence, confidence, expected context reduction, behavior risk, evaluation requirements and a rollback path. A plan exists before mutation.
Parsing, hashing, exact duplication and manifests should not spend model tokens.
Static context estimates, provider telemetry and behavioral parity are separate evidence classes.
Safety boundaries, destructive-operation gates and downstream contracts are not casually compressed.
Provenance, source commit and optimization plans remain attached to compiled artifacts.
Free today. Founding pricing for the waitlist.
Every tool on this site is free to use in your browser today. Paid plans — saved reports, API and CLI access, team governance — open to the founding waitlist first, at founding pricing locked in before public launch.
Join the founding waitlistWhat tokencheat means by optimization.
Is tokencheat just a prompt minifier?+
No. tokencheat optimizes context exposure: what must be active now, what can be discovered on demand, what belongs in references, and what is truly waste. Shorter text matters only if required behavior survives.
What is Context Exposure Reduction?+
It compares typical active instruction context before and after an architectural change. It remains an estimate until provider-reported usage is measured, so estimated, measured and behavioral claims stay separate.
What is the Instruction Architecture Score?+
A diagnostic composite of always-on efficiency, duplication, progressive disclosure, trigger specificity, cohesion, portability, freshness, tool overhead, provenance and validation coverage. It is not a promise of causal dollar savings.
Will tokencheat rewrite third-party skills automatically?+
Not by default. Early optimization is conservative and structural. Unknown or restrictive licenses remain analysis-only for public distribution, and semantic changes require evaluation before an Optimized label.
Is the marketplace the main product?+
No. Measurement, compilation, validation and provenance come first. Distribution is the output of a trustworthy build system, not the starting point.
Audit the architecture before you add more context.
Start with your always-on agent config, then extract reusable workflows into portable skills.