Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add fallow-rs/fallow-skills/plugin install fallowWrote 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/fallow-rs/fallow-skills/fallow-review)<a href="https://agentmods.dev/skills/fallow-rs/fallow-skills/fallow-review"><img src="https://agentmods.dev/badge/skills/fallow-rs/fallow-skills/fallow-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/fallow-rs/fallow-skills/fallow-review"><img src="https://agentmods.dev/badge/skills/fallow-rs/fallow-skills/fallow-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
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 | $0.00151 | $0.04803 |
| Opus 5 | $0.00076 | $0.02402 |
| Sonnet 5 | $0.00030 | $0.00961 |
| Haiku 4.5 | $0.00015 | $0.00480 |
Grade A, and why
fallow-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 6d 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fallow Review: graph-grounded code review
fallow review turns a changeset into a deterministic, graph-derived review brief and an agent-contract loop. It answers "where do I look, and what calls actually need human taste?" rather than "will CI block this?" (that is fallow audit, which gates). review always exits 0, so it is safe to run regardless of verdict.
The four jobs it does, in order:
- subtract: dead code, complexity, duplication, and styling for the changed files are reported and kept OUT of the judgment loop, so attention is not spent on what a deterministic check already owns.
- focus: changed-file units are ranked by a composite attention score (fan-in/out, risk zone, change shape) with a
review-here/not-prioritizedlabel and a fulldeprioritizedescape-hatch list, so nothing is hidden. - structure: the decision surface lifts the handful of consequential STRUCTURAL decisions out of the diff and frames each as a judgment question, capped to a working-memory-sized set, each anchored to a
signal_idfallow emitted. - direct: the walkthrough guide hands an agent a graph-derived digest, the review direction, a graph-snapshot pin, and the exact judgment schema to return.
When to use
- Reviewing a PR, branch, or diff (AI-generated or human-written) before merge.
- After an agent has done work and removed the fallow findings it could; this surfaces what is left for human/agent taste.
- Producing inline-reviewable judgments that can flow back to the agent that wrote the code.
When NOT to use
- Gating CI on a pass/fail verdict: use
fallow audit(it exits non-zero on a fail verdict). - Whole-project health, cleanup, or dead-code reports: use the
fallowskill.
Quick human brief
# Auto-detect the base (merge-base against the upstream / remote default):
fallow review
# Pin the base, or scope to a precise diff:
fallow review --base origin/main
git diff --find-renames origin/main...HEAD | fallow review --base origin/main --diff-stdin
What ships with it
5 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.
- 6d ago Changed · -6 lines fde7c5fee2bb
- 12d ago First seen · 241 lines · 151 tokens per session scan A f4ca5b4e8fd6
fallow-review is a skill published in the GitHub repository fallow-rs/fallow-skills (121 stars, last pushed yesterday), licensed MIT. It adds 151 tokens to every session and 4,803 once invoked, about $0.0008 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 skills, from other repositories
code-review
Review the diff since a fixed point along two axes — Conventions (does the code follow this repo's coding standards and Tidy First practices?) and Intent (does the change do what it claims to do?). Runs both reviews in parallel sub-agents and reports them side by side. Use when you want to review a branch, a PR…
slop
Remove AI-generated code slop from git diffs to maintain code quality.
pr-review
Fix PR review comments by implementing requested changes.
code-quality-review
Run an extremely strict maintainability and structural code quality review — flags abstraction issues, spaghetti growth, and boundary leaks.
code-reviewer
Review code for quality, security, and best practices — read-only analysis.
ai-slop-remover
Remove AI-generated code patterns that don't match codebase style.