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/analyze-specnpx skills add a16z/jolt --skill analyze-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/analyze-spec)<a href="https://agentmods.dev/skills/a16z/jolt/analyze-spec"><img src="https://agentmods.dev/badge/skills/a16z/jolt/analyze-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 5.1 | $0.00018 | $0.01272 |
| Opus 5 | $0.00009 | $0.00636 |
| Sonnet 5 | $0.00004 | $0.00254 |
| Haiku 4.5 | $0.00002 | $0.00127 |
Grade A, and why
analyze-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 6d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill operates in two modes:
- Local mode (invoked via
/analyze-specin Claude Code): Full interactive Socratic interview — one question at a time, iterative refinement with the spec author. - Remote mode (triggered externally via the
claude-spec-review-requestlabel): Single-pass analysis — all questions posted at once as a single PR comment. Reads prior PR comments as context to account for already-answered questions.
Adapted from the Ouroboros-inspired deep interview methodology — specification quality is the primary bottleneck in AI-assisted development.
<Execution_Policy>
- Gather codebase facts via
exploreagent BEFORE asking about them - Cite repo evidence (file path, symbol, or pattern) instead of asking the spec author to rediscover it
- Score ambiguity transparently
- Do not approve until ambiguity ≤ threshold (default 0.2)
- Allow early approval with a clear warning if ambiguity is still high </Execution_Policy>
<Mode_Detection> Detect which mode to use:
- Remote mode: Running in a remote Claude instance triggered by the
claude-spec-review-requestlabel. Indicators: environment is non-interactive (no TTY), or running inside a GitHub PR context. - Local mode: Running interactively in a terminal via
/analyze-spec.
When in doubt, default to local mode (interactive). </Mode_Detection>
Phase 1: Initialize
- Locate the spec:
- If a path is provided in
{{ARGUMENTS}}, use that directly. - Otherwise, detect the PR number:
- Run
gh pr view --json number --jq .numberto get the current branch's PR number. - If that fails, list specs:
ls specs/and ask the user which one.
- Run
- Look for
specs/<PR#>-*.mdmatching the PR number. This is the spec for this PR. - If no match, fall back to finding any
specs/*.mdfile that is NOTTEMPLATE.md. - If multiple specs match, prefer the one matching the PR number. If still ambiguous, ask the user.
- If a path is provided in
- Read the spec thoroughly — understand all sections (Summary, Intent, Evaluation, Design, Execution).
- Read
jolt-eval/README.mdso you understand the invariant/objective framework for scoring Success Criteria and generating questions. - Explore the codebase: Run
exploreagent to map codebase areas relevant to the spec's intent. - Read prior context (remote mode): Read all existing PR comments via
gh pr view --json commentsto identify questions already asked and answers already given. Account for these when scoring — don't re-ask answered questions.
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.
- 6d ago First seen · 104 lines · 18 tokens per session scan A ebc8f7083970
analyze-spec is a skill published in the GitHub repository a16z/jolt (1,022 stars, last pushed today), licensed Apache-2.0. It adds 18 tokens to every session and 1,272 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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