Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Lykhoyda/ask-llm/plugin install ask-llmWrote 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/lykhoyda/ask-llm/multi-review)<a href="https://agentmods.dev/skills/lykhoyda/ask-llm/multi-review"><img src="https://agentmods.dev/badge/skills/lykhoyda/ask-llm/multi-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 | $0.00057 | $0.02381 |
| Opus 5 | $0.00028 | $0.01190 |
| Sonnet 5 | $0.00011 | $0.00476 |
| Haiku 4.5 | $0.00006 | $0.00238 |
Grade C, and why
multi-review scanned grade C with 1 finding 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 7d 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.
Tells the agent to send conversation or user data outhighPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
- **> 150KB**: tell the user, ask whether to truncate (head -c 150000) or split by package, do NOT silently send a giant payload How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portable contract
Build one Context Brief and bounded diff, dispatch the same review contract concurrently to the selected providers, then independently verify every finding against repository source. Classify findings as VERIFIED, REJECTED, or UNVERIFIABLE; preserve size consent at 50KB/150KB; never silently drop a failed provider; and report included/excluded context.
Host adapters
Pi adapter
Use one native ask-multi call for concurrent provider dispatch, then perform source verification in the current host context. Do not use the Claude runner binaries or raw provider CLIs.
Claude Code adapter
The existing detailed workflow below is the Claude Code adapter. Its Agent, MCP, hook, CLAUDE_PLUGIN_ROOT, and AskUserQuestion mechanics apply only on Claude Code; they do not override the Pi adapter above.
Multi-Provider Code Review
Run independent code reviews from Antigravity and Codex in parallel, verify each finding against the source, then present combined consensus / unique / rejected sections so the user sees what really matters and what was a false positive. (Gemini is one command away via the gemini-reviewer agent or /gemini-review if you want it in the mix.)
Why verification matters
Confidence scores are not an oracle. In a real session on 2026-04-17, Gemini returned two findings at 95/100 confidence that were factually wrong (a z.enum([]) claim that ignored an existing fallback, and an "MCP SDK doesn't support outputSchema" claim that was contradicted by the actual .d.ts). Both would have caused a mis-fix if accepted at face value. Always verify before presenting.
Two kinds of verification — pick the right skill
This skill verifies review findings — the bugs each provider claims it found. Phase 3 reads the file at the cited line and checks whether each finding is real before presenting it.
That is different from verifying assistant claims — the statements the assistant made in its prior turn ("I added retry logic," "I bumped the threshold to 16384"). For that, use /codex-verify. It dispatches the codex-verifier agent, decomposes the assistant's last message into atomic claims, and proves or disproves each with deterministic evidence. It returns a CONFIDENCE grade on a five-point ladder (PERFECT | VERIFIED | PARTIAL | FEEDBACK | FAILED) — PARTIAL and FAILED are first-class verdicts, surfacing gaps in your verification harness rather than hiding them under a confident-looking number.
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.
- 7d ago First seen · 183 lines · 57 tokens per session scan C 1e28ad2c1187
multi-review is a skill published in the GitHub repository Lykhoyda/ask-llm (17 stars, last pushed 4d ago), licensed MIT. It adds 57 tokens to every session and 2,381 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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