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.
Borrowing it
Nothing to install: this file belongs to a16z/jolt. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/a16z/jolt/main/.claude/skills/implement-spec/SKILL.mdgit 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/implement-spec)<a href="https://agentmods.dev/skills/a16z/jolt/implement-spec"><img src="https://agentmods.dev/badge/skills/a16z/jolt/implement-spec/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/a16z/jolt/implement-spec"><img src="https://agentmods.dev/badge/skills/a16z/jolt/implement-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01431 |
| Opus 5.5 | $0.00007 | $0.00572 |
| Sonnet 5.5 | $0.00003 | $0.00286 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
implement-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 1mo 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill runs locally or in Claude Code cloud (claude.ai/code) — NOT in CI. It needs write access to the repo to create commits and push to the PR branch.
<Execution_Policy>
- The spec is the source of truth. Implement what it says, not more.
- Read CLAUDE.md for project conventions, testing requirements, and architecture.
- Each phase must complete before the next begins.
- Parallel execution within phases where possible.
- Resolve routine implementation choices from the spec and repository conventions. For ambiguity affecting correctness, scope, or acceptance criteria, post a focused PR question and continue unaffected work.
- Do not add features, refactor code, or make improvements beyond the spec.
- If the spec lacks the
claude-spec-approvedlabel, note that it hasn't been analyzed. An explicit user instruction to implement it is sufficient authorization; otherwise request approval. </Execution_Policy>
Phase 1: Plan
- Read the spec: Find the
specs/*.mdfile in this PR (excludeTEMPLATE.md). If a path is provided in{{ARGUMENTS}}, use that. - Read CLAUDE.md: Understand architecture, conventions, testing requirements.
- Read
jolt-eval/README.md: Understand the eval framework — the spec's Intent → Invariants and Evaluation → Performance sections may reference it. - Explore relevant code: Use
exploreagents to understand the modules, types, and patterns the implementation will touch. - Extract evals: Scan the spec's Intent → Invariants and Evaluation → Performance sections for
jolt-evalreferences and list:- New invariants to add → each becomes a
/new-invariant <name>subtask. - New objectives to add → each becomes a
/new-objective <name>subtask. - Existing invariants/objectives that need to be changed.
- New invariants to add → each becomes a
- Create implementation plan: Based on the spec's Intent and Execution sections, determine:
- Files to create, modify, or remove
- Order of changes (dependencies first)
- How existing patterns and abstractions should be extended
- Which tasks can run in parallel vs. sequential
- Post the plan as a PR comment for visibility:
**Implementation plan for: {spec title}**
**Changes:**
1. {file/module} — {what changes and why}
2. ...
**Order:** {dependency chain}
**Parallel tasks:** {which can run simultaneously}
**Estimated scope:** {number of files, rough line count}
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.
- 1mo ago Changed 0d60eb1d029d
- 1mo ago Changed · +2 lines b6ba67de6f4b
- 2mo ago First seen · 118 lines · 17 tokens per session scan A e95ae54ad63c
implement-spec is a skill published in the GitHub repository a16z/jolt (1,059 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 1,431 once invoked, about $0.0001 per session on Opus 5.5. 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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