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 skills add budagov-lab/DreamTeam --skill reviewer-code-reviewgit clone --depth 1 https://github.com/budagov-lab/DreamTeamWrote 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/budagov-lab/dreamteam/reviewer-code-review)<a href="https://agentmods.dev/skills/budagov-lab/dreamteam/reviewer-code-review"><img src="https://agentmods.dev/badge/skills/budagov-lab/dreamteam/reviewer-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/skills/budagov-lab/dreamteam/reviewer-code-review"><img src="https://agentmods.dev/badge/skills/budagov-lab/dreamteam/reviewer-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 | $0.00031 | $0.00262 |
| Opus 5 | $0.00015 | $0.00131 |
| Sonnet 5 | $0.00006 | $0.00052 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
reviewer-code-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 9d 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.
What it actually says
Reviewer Code Review
When to Use
- After Developer completes a task
- User requests code review
- Before merging significant changes
Workflow
- Read: Changed files, task requirements (MCP dreamteam_get_task or Terminal get-task)
- Verify: Dispatch Terminal subagent → run
pytest. If tests fail → CRITICAL. - Check: Correctness, architecture compliance
- Report: APPROVED or CRITICAL (one line, max 50 words for Critical points)
Checklist
- Logic is correct, edge cases handled
- No security issues
- Follows project style
- Tests cover changes
- No circular dependencies introduced
- Module ownership respected
Output Format (match Reviewer agent)
- APPROVED — tests pass, no critical issues
- CRITICAL: [1–3 bullet points, max 50 words] — must fix before done
One line only. No Suggestion/Nice-to-have — Orchestrator expects APPROVED or CRITICAL.
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.
- 9d ago First seen · 36 lines · 31 tokens per session scan A d64a70ebc92f
reviewer-code-review is a skill published in the GitHub repository budagov-lab/DreamTeam (1 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 262 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
security-review
AI-powered security analysis of code changes — traces data flow, detects injection, auth bypass, secrets exposure, and unsafe deserialization across files. Use when reviewing pending changes, before release-branch, during verify-work Phase 5, during build-epic Step 0 threat modeling, or when the user says "security…
audit-code
Self-review checklist for the coding agent to run before dispatching a reviewer. Checks CONVENTIONS.md compliance, Boy Scout Rule, test coverage, types, and SOLID. Produces a pass/fail checklist. Use before request-review, before committing, or when user asks for a code quality check.
commit-message
Reviews working-tree changes, then drafts a Conventional Commits title/body and states the semantic-release version bump a single such commit would imply. Also notes which defensive-code categories were touched. Use when the user wants to commit recent work, prepare a Conventional Commits message, or asks for…
request-review
Dispatch a fresh reviewer agent with a clean context to critique the code after audit-code passes. The reviewer has no shared state with the coding agent and gives a genuine second opinion. Use after audit-code passes, before committing, or when user wants an independent code review.
respond-review
Act on a reviewer agent's feedback systematically — categorize findings, apply fixes, verify tests still pass. Use after request-review returns a report, or when user wants to work through code review findings.
simulate-agents
Run Mock User and Auditor agents against a feature in fresh contexts before human review. Use after verify-work, before request-review, when user wants pre-review simulation.