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.
git clone --depth 1 https://github.com/AltimateAI/claude-consensusWrote 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/commands/altimateai/claude-consensus/code-review)<a href="https://agentmods.dev/commands/altimateai/claude-consensus/code-review"><img src="https://agentmods.dev/badge/commands/altimateai/claude-consensus/code-review/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/commands/altimateai/claude-consensus/code-review"><img src="https://agentmods.dev/badge/commands/altimateai/claude-consensus/code-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.06239 |
| Opus 5.5 | $0.00007 | $0.02496 |
| Sonnet 5.5 | $0.00004 | $0.01248 |
| Haiku 4.5 | $0.00002 | $0.00624 |
Grade B, and why
code-review scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
[ -f ~/.claude/.env ] && export OPENROUTER_API_KEY=$(grep '^OPENROUTER_API_KEY=' ~/.claude/.env | cut -d= -f2- | tr -d '"') How it starts
The opening of the file, as written. The whole thing — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Model Code Review
Get independent code reviews from multiple AI models (Claude + configured external models), compare findings, and converge on a unified review through structured synthesis and agreement rounds.
Compaction Resilience
This is a long-running command. Context compaction may erase in-memory state mid-run.
- Check resume: Glob
data/scratch/active-progress-consensus-code-review-*.md— find any with StatusIN_PROGRESSand < 24h old. If found, read its SESSION_DIR and skip to first unchecked goal. If Status isCOMPLETEDorFAILED, ignore it. - Write progress: After creating SESSION_DIR, create a unique progress file:
data/scratch/active-progress-consensus-code-review-{SESSION_ID}.md(where SESSION_ID is the random suffix from SESSION_DIR, e.g.X4f2kL) - Mark done: Set Status to
COMPLETEDat end of successful run, orFAILEDon abort - Save incrementally: Write/append to
$SESSION_DIRfiles after each phase, not at the end
Goals Template
# consensus:code-review — {TARGET}
Started: [timestamp]
Status: IN_PROGRESS
Command: consensus:code-review
SESSION_DIR: {SESSION_DIR path}
TTL: 24h
## Goals
- [ ] Phase 1 — Setup: load config, determine review target, write prompt, create team
- [ ] Phase 2 — Spawn reviewers: launch teammate agents + write Claude's code review
- [ ] Phase 3 — Collect reviews: wait for all models to send their reviews
- [ ] Phase 4 — Analyze & compare: build comparison table, identify consensus issues
- [ ] Phase 5 — Synthesize: draft unified code review ordered by severity
- [ ] Phase 6 — Convergence: send draft to all models, collect APPROVE/CHANGES NEEDED
- [ ] Phase 7 — Present final review with attribution table, cleanup team
## Progress
- [HH:MM] Starting execution...
Checkpoints
- After Phase 2: Claude's review written to
$SESSION_DIR/claude.md - After Phase 3: All model reviews on disk at
$SESSION_DIR/{model.id}.md - After Phase 5: Draft review at
$SESSION_DIR/draft.md - After Phase 6: Convergence responses at
$SESSION_DIR/{model.id}-convergence.md
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 First seen · 513 lines · 18 tokens per session scan B 3e6ee26cfc1a
code-review is a command published in the GitHub repository AltimateAI/claude-consensus (33 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 6,239 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
review-pr
PR review using parallel specialized agents for code quality, security, testing, architecture, and performance analysis. Synthesizes findings into a review report with conventional comments (praise/issue/suggestion/nitpick) and approve or request-changes verdict. Use when reviewing pull requests, conducting security…
assess
Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use…
review-pr
Review a pull request against an authorized reviewer's brief — answer their questions, examine their angles, run their validations, and converse until they say done. Change NO code.
clean-check
Analyze code for cleanliness issues (unused code, comment quality, formatting, naming, complexity). Delegates to the code-cleanliness agent.
gc
On-demand code-drift / entropy scan (garbage collection) via a fresh-context drift-analyst subagent. Checks changed/active code against golden-rules.md + generic drift heuristics (dead code, duplication, doc-drift) and reports findings with an evidence path. Read-only — recommends, never auto-refactors. Not git gc.
moodle-codestyle
You will lint and fix Moodle coding-style issues.