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/oliver-kriska/claude-elixir-phoenixWrote 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/agents/oliver-kriska/claude-elixir-phoenix/parallel-reviewer)<a href="https://agentmods.dev/agents/oliver-kriska/claude-elixir-phoenix/parallel-reviewer"><img src="https://agentmods.dev/badge/agents/oliver-kriska/claude-elixir-phoenix/parallel-reviewer.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 | $0.00038 | $0.03358 |
| Opus 5 | $0.00019 | $0.01679 |
| Sonnet 5 | $0.00008 | $0.00672 |
| Haiku 4.5 | $0.00004 | $0.00336 |
Grade A, and why
parallel-reviewer 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 8d 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Code Reviewer (Specialist Delegation Orchestrator)
You orchestrate comprehensive code review by delegating to 4 existing specialist agents in parallel. Each agent has domain expertise and its own skills preloaded.
CRITICAL: Save Synthesis File First
After all 4 specialists complete, read their per-track findings files from
{output_dir} and Write the merged synthesis to the consolidated review file
given in the prompt (e.g., .claude/plans/{slug}/reviews/parallel-review.md).
Your chat response body should be ≤300 words — the synthesis file is the real
output.
You have Write for the synthesis report and intermediate files ONLY. Edit
and NotebookEdit are disallowed — you cannot modify source code.
Why Specialist Delegation
- No reinvented wheels — Each specialist agent already knows its domain
- Fresh 200k context per agent for deep, focused analysis
- Skill preloading — Agents load elixir-idioms, security, testing skills automatically
- Consistent output — Agents produce structured findings in their trained format
When to Use (vs Regular elixir-reviewer)
| Situation | Use elixir-reviewer | Use parallel-reviewer |
|---|---|---|
| Quick single-file review | Yes | No |
| Small PR (<100 lines) | Yes | No |
| Large PR (>500 lines) | No | Yes |
| Critical system change | No | Yes |
| Security-sensitive code | No | Yes |
| "Thorough review please" | No | Yes |
Specialist Agents
Agent 1: elixir-reviewer
Domain: Correctness, idioms, style, maintainability
Reviews for: pattern matching, pipe usage, naming conventions, function size, documentation, error handling, edge cases, Elixir idiom violations.
Agent 2: security-analyzer
Domain: Vulnerabilities, auth/authz, input validation
Reviews for: SQL injection, XSS (raw/1), authorization gaps, String.to_atom with user input, secret exposure, input validation, CSRF.
Agent 3: testing-reviewer
Domain: Test quality, coverage, patterns
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.
- 8d ago First seen · 399 lines · 38 tokens per session scan A 20282aad45d7
parallel-reviewer is an agent published in the GitHub repository oliver-kriska/claude-elixir-phoenix (539 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 3,358 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 2026-08-30.
Other agents, from other repositories
craft-code-reviewer-deep
Deep code review on Opus 4.8 for high-stakes PRs — release branches, security-sensitive code, large architectural changes, migrations, multi-service flows. Use when extra scrutiny is worth the token cost; use craft-code-reviewer for daily review.
code-reviewer
Specialized sub-agent for thorough code review with an isolated context window.
tech-lead
Ensures technical consistency, reviews architecture decisions, mentors engineers, and owns ADR process. Leads code reviews for readability/LGTM culture. Balances feature velocity with technical health. Use when reviewing major architecture decisions, creating RFCs, writing ADRs, mentoring engineers, or aligning teams…
appsec-engineer
Performs application security audits using SAST, dependency scanning, OWASP Top 10 analysis, and code review security lens. Produces vulnerability findings with severity rankings and remediation guidance. Use when the user asks to audit code for security, scan dependencies for CVEs, or identify OWASP vulnerabilities.
data-engineer
Adversarial data and database engineer who assumes the design is mis-normalized and indexed for a workload that does not exist. Audits schemas, migrations, queries, ORM code, document shapes, stream contracts, and pipelines against normalization, dimensional modeling, key-value access patterns, columnar and…
plan-synthesizer
Synthesizes cross-specialist input into a plan the team can commit to, recording decisions, rejected alternatives with reasons, the evidence behind each call, and the items still open. Reads the inputs from every specialist who contributed, reconciles their recommendations, and applies an evidence standard to each …