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 alyssa-dahlberg/agent-skills --skill review-lensesgit clone --depth 1 https://github.com/alyssa-dahlberg/agent-skillsWrote 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/alyssa-dahlberg/agent-skills/review-lenses)<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.
<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>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 | $0.00181 | $0.03027 |
| Opus 5 | $0.00090 | $0.01514 |
| Sonnet 5 | $0.00036 | $0.00605 |
| Haiku 4.5 | $0.00018 | $0.00303 |
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.
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:
- Triggers matched → run it. Any lens whose
triggersglobs match a changed path is in — that's the floor. - 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.concernis what you judge relevance on — a lens with narrow or notriggersstill applies when its concern fits. Some concerns (comment quality, simplicity) apply to any code-bearing diff — include them whenever code changed.
- Triggers matched → run it. Any lens whose
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.
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.
- 12d ago First seen · 177 lines · 181 tokens per session scan A 6561233dee79
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.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…