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 5.5 | $0.00072 | $0.01211 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 3mo ago), licensed MIT. It adds 181 tokens to every session and 3,027 once invoked, about $0.0007 per session on Opus 5.5. 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.
review-lenses compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| review-lensesalyssa-dahlberg/agent-skills | Skill | 2 | 3mo ago | MIT | A | 181 | Claude Code |
| autoreviewopenclaw/clawhub | Skill | 9.5k | today | MIT | A | 17 | Claude Code, Codex |
| omh-code-reviewrlaope/oh-my-hermes | Skill | 5.2k | today | MIT | A | 48 | Claude Code, Codex |
| agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 115 | Claude Code, Codex |
| revdiff-planumputun/revdiff | Skill | 947 | 2d ago | MIT | A | 84 | Claude Code |
| explore-unknownsdzhng/skills | Skill | 1.4k | today | MIT | A | 162 | Claude Code, Codex |
| re0-mergeLilMGenius/paperthin | Skill | 1.4k | 4d ago | MIT | A | 70 | Claude Code |