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 zszz3/AgentRecall --skill dsh-code-reviewgit clone --depth 1 https://github.com/zszz3/AgentRecallWrote 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/zszz3/agentrecall/dsh-code-review)<a href="https://agentmods.dev/skills/zszz3/agentrecall/dsh-code-review"><img src="https://agentmods.dev/badge/skills/zszz3/agentrecall/dsh-code-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/zszz3/agentrecall/dsh-code-review"><img src="https://agentmods.dev/badge/skills/zszz3/agentrecall/dsh-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.00525 |
| Opus 5.5 | $0.00017 | $0.00210 |
| Sonnet 5.5 | $0.00008 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
dsh-code-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 1mo 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Produce a findings-first review grounded in the live diff, surrounding implementation, current consumers, and the target repository's rules. Prefer one substantiated blocker over many speculative nits.
Orient the review
- Read the applicable
AGENTS.mdfiles and owning subsystem documentation. - Verify the live base and exact head. Refresh them after a retarget, merge, or new commit.
- Inspect the diff, then read enough unchanged code, tests, call sites, and entrypoints to understand behavior and ownership.
- Identify which checks ran, but do not treat green automation as semantic proof.
Review priorities
- Correctness and contracts: trace both sides of changed interfaces, including errors, cancellation, ordering, durability, and compatibility.
- Lifecycle and concurrency: verify ownership and deterministic cleanup for timers, listeners, subprocesses, watchers, leases, callbacks, and temporary resources.
- Security and enforcement: follow permissions and validation to the operation that performs the action; check alternate callers and renderer/main or client/server boundaries.
- Consumer fit: challenge new public APIs, options, abstractions, compatibility paths, and duplicated state without current consumers.
- Untrusted data: validate files, durable records, IPC, network, subprocess, model/tool JSON, and user input at their real boundaries without duplicating same-process type checks.
- Bounds: apply limits to the complete retained or emitted value, including wrappers, metadata, multibyte input, and oversized single items.
- Tests: require observable behavior through the shipped entry path and a negative control that would fail for the intended regression.
- Documentation and prose: verify current behavior, defaults, configuration, failures, and limitations. Remove review history and implementation narration from durable surfaces.
- Packaging and platforms: check generated assets, install/update paths, browser safety, and Windows/macOS differences when the diff crosses those surfaces.
What ships with it
1 file 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.
- 1mo ago First seen · 34 lines · 42 tokens per session scan A 4f32fc5f79d5
dsh-code-review is a skill published in the GitHub repository zszz3/AgentRecall (594 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 525 once invoked, about $0.0002 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 2026-08-30.
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