Thunderbolt is an open-source AI client that lets people connect their chosen models and run the client on their own infrastructure, including local or on-premises deployments. It is aimed at organizations and individuals who want to use different model providers while keeping control of their data.
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/thunderbird/thunderbolt/thunder-deep-reviewnpx skills add thunderbird/thunderbolt --skill thunder-deep-reviewgit clone --depth 1 https://github.com/thunderbird/thunderboltWrote 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/thunderbird/thunderbolt/thunder-deep-review)<a href="https://agentmods.dev/skills/thunderbird/thunderbolt/thunder-deep-review"><img src="https://agentmods.dev/badge/skills/thunderbird/thunderbolt/thunder-deep-review.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 | $0.00000 | $0.06597 |
| Opus 5 | $0.00000 | $0.03299 |
| Sonnet 5 | $0.00000 | $0.01319 |
| Haiku 4.5 | $0.00000 | $0.00660 |
Grade A, and why
thunder-deep-review 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thunder Deep Review
A read-only senior-grade PR reviewer for thunderbird/thunderbolt. It reproduces the house bar — an architecture-and-taste reviewer first, a linter second. It catches the high-value issues generic bug-bots miss: wrong abstraction layer, coupling, undocumented intent, convention drift, bloat/deps, and "this will haunt us" maintainability costs — alongside real correctness/security bugs.
The "house bar" is a standard, not a person. Never name or impersonate any individual. Encode the bar, not the human.
Deployment context — determine this FIRST
One engine, two products. Detect your mode from OBSERVABLE signals in the invoking prompt, then obey it everywhere the steps below split on mode:
- CI / gated mode — you are in this mode iff the invoking prompt does any of: supplies a skip-list file path, supplies a deep-mode/bounded-mode flags file path, or requests structured JSON output against a provided schema and states that a downstream precision gate filters your candidates. Here you are the RECALL pass: a downstream precision gate (a second model step) filters your candidates and is allowed to drop most of them — so favor recall. Surface every grounded candidate even when unsure it clears the bar; report marginal or low-certainty findings with
confidence: lowinstead of dropping them. Your output is the structured JSON the invoking prompt defines — no markdown report, no severity trailer. - Local / ungated mode — anything else (a developer invoked you ad hoc). There is NO gate behind you: your rendered markdown report (exact format:
assets/finding-template.md) is the final artifact, so apply the full filtering pipeline below (Pass B, recall floors, noise suppression, self-validation).
When signals are ambiguous, assume LOCAL mode — an unfiltered candidate dump on a developer is worse than filtering twice.
In both modes: identifying the issue is the job; a suggested fix is optional, never required.
Operating rules (low freedom — always)
What ships with it
7 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 · 170 lines · 0 tokens per session scan A fcdea08ad494
thunder-deep-review is a skill published in the GitHub repository thunderbird/thunderbolt (4,761 stars, last pushed today), licensed MPL-2.0. It costs nothing until one of its globs matches a file; then it loads 6,597 tokens. 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.
Other skills, from other repositories
dingtalk_channel_connect
Use a headed browser to automatically complete DingTalk channel integration for QwenPaw. Applicable when the user mentions DingTalk, developer console, Client ID, Client Secret, bot, Stream mode, binding or configuring a channel. Supports pausing when a login page is detected and resuming after the user logs in.
thinking-out-loud
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's…
当用户需要对PDF文件进行任何操作时，请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片，以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF，请使用此技能。.
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV…
channel_message
Use this skill to proactively send a one-way message to a user/session/channel, usually only when the user explicitly asks to send to a channel/session or when proactive notification is needed. First query sessions with qwenpaw chats list, then push with qwenpaw channels send.
make_plan
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via listagents and chatwithagent, emphasizing that the plan is executed by the requester, not by the consulted Agent.