Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add quillai-network/quillshield_skills --skill state-invariant-detectiongit clone --depth 1 https://github.com/quillai-network/quillshield_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/quillai-network/quillshield_skills/state-invariant-detection)<a href="https://agentmods.dev/skills/quillai-network/quillshield_skills/state-invariant-detection"><img src="https://agentmods.dev/badge/skills/quillai-network/quillshield_skills/state-invariant-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/quillai-network/quillshield_skills/state-invariant-detection"><img src="https://agentmods.dev/badge/skills/quillai-network/quillshield_skills/state-invariant-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01968 |
| Opus 5 | $0.00046 | $0.00984 |
| Sonnet 5 | $0.00019 | $0.00394 |
| Haiku 4.5 | $0.00009 | $0.00197 |
Grade A, and why
state-invariant-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
State Invariant Detection
Automatically infer mathematical relationships between state variables, then find functions that break those relationships. Catches the most devastating DeFi vulnerabilities: unauthorized minting, broken tokenomics, accounting discrepancies, and state desynchronization.
When to Use
- Auditing token contracts for supply/balance mismatches
- Analyzing staking, vault, or pool contracts for accounting errors
- Detecting conservation law violations in treasury/fund management
- Finding AMM/DEX constant product violations
- Verifying that aggregate variables stay synchronized with individual records
When NOT to Use
- Guard-state consistency analysis (use semantic-guard-analysis)
- Full multi-dimensional audit (use behavioral-state-analysis)
- Entry point identification only (use entry-point-analyzer)
Core Concept: State Variable Proportionality
Hypothesis: In well-designed contracts, state variables maintain mathematical relationships (invariants) that should never be violated.
When a function modifies one side of a relationship without updating the other, the invariant breaks — creating exploitable accounting errors.
Five Types of State Relationships
Type 1: Sum Relationships (Aggregation)
totalSupply = Σ balance[i] for all users i
Found in: ERC20 tokens, staking pools, vaults, share systems
Type 2: Difference Relationships (Conservation)
totalFunds = availableFunds + lockedFunds
Found in: Treasuries, liquidity pools, vesting contracts
Type 3: Ratio Relationships (Proportional)
k = reserveA × reserveB (constant product)
sharePrice = totalAssets / totalShares
Found in: AMMs, DEXs, vault share pricing, collateralization
Type 4: Monotonic Relationships (Ordering)
newValue ≥ oldValue (only increases)
Found in: Timestamps, nonces, accumulated rewards, total distributions
Type 5: Synchronization Relationships (Coupling)
If stateA changes, stateB must change correspondingly
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 253 lines · 93 tokens per session scan A d2a96edddce1
state-invariant-detection is a skill published in the GitHub repository quillai-network/quillshield_skills (121 stars, last pushed 5mo ago), licensed MIT. It adds 93 tokens to every session and 1,968 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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