Jolt is a zero-knowledge virtual machine for RISC-V that enables programs to be executed and cryptographically proven. It is for developers and contributors working with zero-knowledge proofs and virtual machines, while the catalogue entries provide instructions and skills for using or developing Jolt.
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 agentmods add skills/a16z/jolt/new-specnpx skills add a16z/jolt --skill new-specgit clone --depth 1 https://github.com/a16z/joltWrote 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/a16z/jolt/new-spec)<a href="https://agentmods.dev/skills/a16z/jolt/new-spec"><img src="https://agentmods.dev/badge/skills/a16z/jolt/new-spec.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.01525 |
| Opus 5 | $0.00010 | $0.00763 |
| Sonnet 5 | $0.00004 | $0.00305 |
| Haiku 4.5 | $0.00002 | $0.00153 |
Grade A, and why
new-spec 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 5d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a new spec file in specs/ by interviewing the user to fill each section of the template. The goal: produce a spec that would pass /analyze-spec with zero ambiguity on the first try.
Steps
1. Initialize
- Validate the argument: must be lowercase alphanumeric with dashes (e.g.
streaming-prover). Reject otherwise. - Get the GitHub username: run
gh api user --jq .login. - Get today's date in
YYYY-MM-DDformat. - Read
specs/TEMPLATE.mdto understand the required sections. - Read
jolt-eval/README.mdto understand the eval framework — you'll reference it when prompting the user about mechanical verifiability. - Explore the codebase to understand what areas the feature name suggests — run an
exploreagent to gather context. This informs your questions.
2. Interview — Section by Section
Walk through each template section with the user. For each section, ask targeted questions until you have enough to write it with zero ambiguity. Do NOT move to the next section until the current one is clear.
One question at a time. Never batch.
Summary
Ask: "In one paragraph, what problem does this solve and why does it matter?"
Intent — Goal
Ask: "What are we building? Can you state the primary objective in one sentence?" Follow up on abstractions, types, boundaries if unclear.
Intent — Invariants
Before asking the user anything, form a hypothesis from the Summary and Goal:
- Read
jolt-eval/src/invariant/(listed injolt-eval/README.md). For each existing invariant, judge whether this feature is likely to touch it. If yes, note whether the feature looks like it should preserve the invariant as-is or modify it (e.g., extend input type, change the reference implementation). - Independently, imagine the binary properties that would have to hold for the Goal to be "correct." Which of those are not yet covered by an existing
jolt-evalinvariant? Each is a candidate for/new-invariant.
Present the hypothesis to the user in one pass:
Based on the Summary and Goal, I think this feature likely:
- May need to modify: {existing invariant Z — because ...}
- Warrants new invariants: {A — "...", B — "..."}
Does this match your intent? What would you add, remove, or change?
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.
- 5d ago First seen · 125 lines · 20 tokens per session scan A e91586f2c052
new-spec is a skill published in the GitHub repository a16z/jolt (1,022 stars, last pushed today), licensed Apache-2.0. It adds 20 tokens to every session and 1,525 once invoked, about $0.0001 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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