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a16za16z/jolt/.claude/skills/new-invariant/SKILL.md

new-invariant

Skill1.1k starsChanged 13 days agoApache-2.0View on GitHub

Implement a new invariant for jolt-eval

We split this skill into the rules it gives an agent

18 rules. About 0 are common, 6 are distinctive, and 12 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 12

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the request, spec, and repository context to establish the property and input type.

    line 15
  2. Distinctive:

    Follow existing patterns exactly — study the split_eq_bind and soundness invariants as models.

    line 16
  3. Distinctive:

    Always run clippy and the auto-generated tests before reporting success.

    line 17
  4. Very specific:

    Read jolt-eval/src/invariant/mod.rs to understand the current JoltInvariants enum and dispatch!

    line 33
  5. Distinctive:

    Read an existing invariant for reference:

    line 34
  6. Very specific:

    Create the invariant file at jolt-eval/src/invariant/<invariant_name>.rs with:

    line 41
  7. Very specific:

    Must derive Debug, Clone, Serialize, Deserialize, JsonSchema

    line 59
  8. Very specific:

    Must implement Arbitrary manually (for fuzzing)

    line 60
  9. Very specific:

    Use bounded ranges in Arbitrary impl (e.g.

    line 61
  10. Very specific:

    Use CheckError::InvalidInput for degenerate inputs that should be skipped (not counted as violations)

    line 90
  11. Very specific:

    Use CheckError::Violation(InvariantViolation::with_details(...)) for actual violations — include diagnostic info

    line 91
  12. Very specific:

    Add pub mod <invariant_name>.

    line 98
  13. Very specific:

    Add a variant to JoltInvariants:

    line 99
  14. Very specific:

    Add the variant to JoltInvariants::all():

    line 103
  15. Very specific:

    Use <Name>Invariant::default() if the struct has fields.

    line 107
  16. Very specific:

    Add the variant to the dispatch!

    line 108
  17. Distinctive:

    Run these commands (all must pass):

    line 127
  18. Distinctive:

    If any step fails, fix the issue and re-run.

    line 143

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<Purpose> Implement a new invariant for the jolt-eval framework. An invariant is a property that must always hold — the framework can test it with seed inputs, fuzz it with random/structured inputs, and red-team it with an AI agent.

This skill handles all the boilerplate: creating the invariant struct + input type, implementing the Invariant trait, registering it in the JoltInvariants enum, creating a fuzz target (if applicable), and running sync_targets.sh. </Purpose>

<Execution_Policy>

  • The user must provide an invariant name (lowercase with underscores, e.g. sumcheck_binding).
  • Use the request, spec, and repository context to establish the property and input type; ask only for missing requirements before writing code.
  • Follow existing patterns exactly — study the split_eq_bind and soundness invariants as models.
  • Always run clippy and the auto-generated tests before reporting success. </Execution_Policy>
<Steps>

Phase 1: Gather Requirements

  1. Validate the argument {{ARGUMENTS}}: must be a valid Rust identifier (lowercase alphanumeric + underscores). Reject otherwise.
  2. Gather these requirements from existing context; ask the user only for missing information:
    • What property does this invariant check? (becomes the description())
    • What does the input look like? (fields, types, ranges)
    • What synthesis targets should it support? (Test, Fuzz, RedTeam)
    • Does it need non-trivial setup? (e.g. preprocessing, compilation — default to Setup = ())

Phase 2: Explore Context

  1. Read jolt-eval/src/invariant/mod.rs to understand the current JoltInvariants enum and dispatch! macro.
  2. Read an existing invariant for reference:
    • Simple: jolt-eval/src/invariant/split_eq_bind.rs
    • Complex (with setup, enrich_input): jolt-eval/src/invariant/soundness.rs
  3. Explore the modular crates that own the tested types and APIs.

Phase 3: Implement

Create the invariant file at jolt-eval/src/invariant/<invariant_name>.rs with:

Input Type

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize, schemars::JsonSchema)]
pub struct <Name>Input {
    // fields
}

impl<'a> Arbitrary<'a> for <Name>Input {
    fn arbitrary(u: &mut Unstructured<'a>) -> arbitrary::Result<Self> {
        // Generate random inputs with reasonable bounds
    }
}

Key requirements for the input type:

  • Must derive Debug, Clone, Serialize, Deserialize, JsonSchema
  • Must implement Arbitrary manually (for fuzzing)
  • Use bounded ranges in Arbitrary impl (e.g. u.int_in_range(2..=16)?) to keep inputs meaningful

Invariant Struct

#[jolt_eval_macros::invariant(Test, Fuzz, RedTeam)]  // adjust targets as needed
#[derive(Default)]
pub struct <Name>Invariant;

impl Invariant for <Name>Invariant {
    type Setup = ();  // or a custom setup type
    type Input = <Name>Input;

    fn name(&self) -> &str { "<invariant_name>" }
    fn description(&self) -> String { "...".into() }
    fn setup(&self) -> Self::Setup { /* ... */ }
    fn check(&self, setup: &Self::Setup, input: Self::Input) -> Result<(), CheckError> {
        // 1. Validate input — return Err(CheckError::InvalidInput(...)) for degenerate cases
        // 2. Run the property check
        // 3. Return Ok(()) if the invariant holds
        // 4. Return Err(CheckError::Violation(...)) if violated
    }
    fn seed_corpus(&self) -> Vec<Self::Input> {
        // Include: minimal case, typical case, edge case (large values, boundary conditions)
    }
}

Guidelines for check()

  • Use CheckError::InvalidInput for degenerate inputs that should be skipped (not counted as violations)
  • Use CheckError::Violation(InvariantViolation::with_details(...)) for actual violations — include diagnostic info
  • Compare against a known-correct reference implementation when testing optimized code

Phase 4: Register

Edit jolt-eval/src/invariant/mod.rs:

  1. Add pub mod <invariant_name>; to the module declarations at the top.
  2. Add a variant to JoltInvariants:
    <VariantName>(<invariant_name>::<Name>Invariant),
  3. Add the variant to JoltInvariants::all():
    Self::<VariantName>(<invariant_name>::<Name>Invariant),
    Use <Name>Invariant::default() if the struct has fields.
  4. Add the variant to the dispatch! macro:
    JoltInvariants::<VariantName>($inv) => $body,

Phase 5: Create Fuzz Target (if targets include Fuzz)

Create jolt-eval/fuzz/fuzz_targets/<invariant_name>.rs:

#![no_main]
use jolt_eval::invariant::<invariant_name>::<Name>Invariant;
jolt_eval::fuzz_invariant!(<Name>Invariant::default());

Then run ./jolt-eval/sync_targets.sh to update fuzz/Cargo.toml.

Phase 6: Validate

Run these commands (all must pass):

# Format
cargo fmt -q

# Lint
cargo clippy -p jolt-eval -q --all-targets -- -D warnings

# Run auto-generated tests (seed_corpus + random_inputs)
cargo nextest run -p jolt-eval --cargo-quiet invariant::<invariant_name>

# If fuzz target was created, verify it compiles
cd jolt-eval/fuzz && cargo check 2>&1 | head -20

If any step fails, fix the issue and re-run.

</Steps>

Task: Implement a new invariant for jolt-eval. {{ARGUMENTS}}

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