review-lenses

review-lenses is a skill for Claude Code from alyssa-dahlberg/agent-skills. It costs 181 tokens per session (3,027 once invoked), scanned A, original, MIT.

A review coordinator that examines code, plans, designs, or ideas through selected viewpoints, called lenses. It breaks a request into separate checks and combines the results into one report.

In plain words
What is it for?
Use it to review a branch, check an architecture, assess a payment flow for PCI security concerns, or sanity-check an idea.
Why use it?
It helps uncover different kinds of problems without requiring you to write a separate review request for each concern.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths; mentions subagents; names the AskUserQuestion tool.

Good fit Use it to review a branch, check an architecture, assess a payment flow for PCI security concerns, or sanity-check an idea.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alyssa-dahlberg/agent-skills/review-lenses
Install

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.

Any agent
npx skills add alyssa-dahlberg/agent-skills --skill review-lenses
Clone the repo
git clone --depth 1 https://github.com/alyssa-dahlberg/agent-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for review-lenses

README.md
[![agentmods](https://agentmods.dev/badge/skills/alyssa-dahlberg/agent-skills/review-lenses/github.svg)](https://agentmods.dev/skills/alyssa-dahlberg/agent-skills/review-lenses)
Your own site
<a href="https://agentmods.dev/skills/alyssa-dahlberg/agent-skills/review-lenses"><img src="https://agentmods.dev/badge/skills/alyssa-dahlberg/agent-skills/review-lenses/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.

agentmods 80×15 button for review-lenses

Your own site · 80×15
<a href="https://agentmods.dev/skills/alyssa-dahlberg/agent-skills/review-lenses"><img src="https://agentmods.dev/badge/skills/alyssa-dahlberg/agent-skills/review-lenses.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 181 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,027 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00181 $0.03027
Opus 5 $0.00090 $0.01514
Sonnet 5 $0.00036 $0.00605
Haiku 4.5 $0.00018 $0.00303

Measured 12d ago against content hash 6561233dee79, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

review-lenses 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 12d 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.

skills/review-lenses/SKILL.md · 177 lines

How it starts

The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review lenses

You are the review orchestrator. You are the brain; the reviewers are headless workers. Your job: turn a request (prose) into a review plan — a set of jobs, each one (lens × target) — dispatch them, and collate one report.

Review is self-serve. This skill isn't only for when a human types a review request — any agent should invoke it on its own work before handing it back: before finalizing a change, after anything security-sensitive, whenever unsure. It's cheap and read-only, and the report comes back structured so a calling agent can act on the findings directly. Reach for it proactively; you don't need to be asked.

Two axes, kept independent: a lens = what to look for (a file in .review-lenses/); a target = what to look at (a diff, a class, a plan phase, a whole plan, a design doc, an idea). Any lens applies to any target. A job is one lens on one target. See reference/lens-format.md for the lens file format and reference/reviewer-brief.md for the brief you hand each reviewer.

Step 1 — Resolve the request into a plan

Read the request and produce { jobs: [{lens, target, context, level}], mode }. You clarify here because reviewers can't — they run headless. Only ask when genuinely ambiguous; otherwise infer and proceed (use AskUserQuestion for real forks like "phase 3 — the evaluator or the migration?"). AskUserQuestion only works when you're the main-loop agent — if this skill was invoked from inside a subagent (an agent self-reviewing its work), infer and proceed, leaning on UNRESOLVED findings for anything you can't settle.

Find the lenses. The lens directory is $REVIEW_LENSES_DIR if that environment variable is set, otherwise .review-lenses/ at the repo root:

echo "${REVIEW_LENSES_DIR:-.review-lenses}"

REVIEW_LENSES_DIR lets a project point at a custom location; how you set an env var depends on your harness (e.g. in Claude Code, add it to .claude/settings.json under "env"). Read the frontmatter of every *.md in that directory except README.md — take concern, triggers, level. If no lens directory exists yet, say so and point the user at the examples shipped with this skill.

  • Directed request ("review the testing of X") → the named lens(es), regardless of triggers.
  • Auto request ("review this branch") → triggers are a starting point, not the whole selection. Do two passes:
    1. Triggers matched → run it. Any lens whose triggers globs match a changed path is in — that's the floor.
    2. Concern reads relevant → also run it. Go through the rest of the lenses and read each one's concern; if a lens's concern is clearly relevant to this change even though no glob fired, add it. concern is what you judge relevance on — a lens with narrow or no triggers still applies when its concern fits. Some concerns (comment quality, simplicity) apply to any code-bearing diff — include them whenever code changed.

Read the full file on GitHub · 177 lines

Files

What ships with it

9 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.

Changes

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.

  1. 12d ago First seen · 177 lines · 181 tokens per session scan A 6561233dee79

Subscribe to this mod's changes

review-lenses is a skill published in the GitHub repository alyssa-dahlberg/agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 181 tokens to every session and 3,027 once invoked, about $0.0009 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.