Insert config · unlock the Cheat Code

Find your agent'sCheat Code.

Audit the instructions your coding agents load on every turn. TokenCheat finds conflicts, stale rules, and wasted context—then generates a tested configuration you can review before it changes anything.

Run free auditSee the token breakdown
Runs in your browserNo signupNo source upload
TokenCheat // context console

Cheat code generated

agent-instructions.tc

Before

3,820

after 1,140

Tokens saved

2,680

Illustrative active-context estimate

01DROPPaste or select your agent instructions
02SCANFind duplicated, stale, and always-on rules
03PLAYApply the tested Cheat Code
Stage clearBEHAVIOR PRESERVED
Works with // agent CLIs
Claude Code$ claude
OpenAI Codex CLI$ codex
Google Gemini CLI$ gemini
GitHub Copilot CLI$ copilot
Cursor Agent$ cursor-agent
Kiro CLI$ kiro
SYS 0145models with verified per-million pricing
SYS 0250dated price snapshots, committed to git
SYS 0318free tools, running in your browser
SYS 04Approximately 30,237,423tokens saved in our own testing — estimated, not customer-reported
Recognise any of these?

The instructions drifted. The agent did not tell you.

None of these announce themselves. They show up as an agent that used to be reliable and now needs watching — which reads as the model getting worse, not as the instructions around it accumulating.

  • WARN 01Important rules get ignored.
  • WARN 02Different coding agents receive different policies.
  • WARN 03Old project decisions stay loaded forever.
  • WARN 04Destructive actions have no clear approval boundary.
  • WARN 05The same instruction appears in several files.
  • WARN 06Nobody can explain why a rule activated.
  • WARN 07Shortening an instruction file quietly removes capability.
instruction architecture — context exposure
The stronger optimization model

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.

illustrative architecture
Always-on core ............. 420
Skill discovery ............. 90
On-demand workflow ....... 2,100
Deferred references ...... 1,150
Waste / obsolete ........... 240
Typical active exposure ≈ 510 tokens
Illustrative example — not provider-measured usage.
The tokencheat loop

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.

Runtime / Policy / Capability

Separate the framework from the expertise.

LAYER 01

Runtime

Session setup, state, host behavior, checkpoints, artifact paths and operating mechanics. Repeated runtime is a strong structural optimization target when evidence is clear.

LAYER 02

Policy

Cross-cutting safety, evidence, completeness, interaction and output rules. Share or defer policy only when required behavior remains explicit.

LAYER 03

Capability

The domain methodology that makes a skill valuable. tokencheat protects capability aggressively instead of treating every long instruction as waste.

Workflows

Measure, audit, optimize, validate.

Agent Config Audit

Live

Score always-on instructions, surface structural waste, and generate conservative deterministic fixes.

Open workflow →

Prompt → Skill

Live

Move reusable workflows out of always-on context and package them as portable SKILL.md artifacts.

Open workflow →
Optimization levels

Not every optimization deserves the same trust.

L0
Audit only
No transformation
L1
Structural deduplication
Low risk
L2
Progressive disclosure
Low–medium risk
L3
Semantic compression
Evaluation required
L4
Lite build
Intentional capability reduction
Evidence before claims

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.

Deterministic first

Parsing, hashing, exact duplication and manifests should not spend model tokens.

Estimated ≠ measured

Static context estimates, provider telemetry and behavioral parity are separate evidence classes.

Critical constraints stay locked

Safety boundaries, destructive-operation gates and downstream contracts are not casually compressed.

Reversible builds

Provenance, source commit and optimization plans remain attached to compiled artifacts.

Pricing

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 waitlist
FAQ

What 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.

Run the config audit →Open Skill Compiler
Inside the product

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.

Join the founding waitlist
tokencheat workspace — previewillustrative data

Saved audits

24

Average context score

71

Best efficiency score

94

Est. monthly savings

$412

ReportScoreSavings

claude-code · CLAUDE.md

Aug 18

88$96

cursor rules · monorepo

Aug 16

72$54

support-agent system prompt

Aug 14

64$131

mcp servers · 6 enabled

Aug 12

51$88

review-bot instructions

Aug 09

77$43
Free tools

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.

All 18 tools

Audit

Team favoriteLive

Agent Config Audit

Score and conservatively optimize always-on agent instruction files in the browser.

Open tool →

Optimize

Team favoriteLive

Agent Cheat Code

Compile your durable working conditions, technology policy, and action boundaries into one portable Cheat Code, then export adapters for Claude Code, Cursor, Copilot, and AGENTS.md.

Open tool →

Measure

Team favoriteLive

AI Token Calculator

Estimate LLM cost from input, output, and cache tokens using maintained per-million pricing. Estimates only — verify against your provider invoice.

Open tool →

Measure

Team favoriteLive

Model Price Comparison

Provider-by-provider view of per-million input and output rates with context windows, sourced from provider pricing pages.

Open tool →

Optimize

Live

Skill Compiler

Start with Prompt → Skill today; inspect the full roadmap for repo audits, structural builds, host packages, provenance, and behavioral validation.

Open tool →

Optimize

Live

Technology and Action Register

Review all twenty-one capability decisions as one sortable table and every action an agent may take on your behalf, with the authorization level each one sits at. States policy; the host enforces it.

Open tool →