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 Jessinra/Lorekeeper --skill requesting-code-reviewgit clone --depth 1 https://github.com/Jessinra/LorekeeperWrote 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/jessinra/lorekeeper/requesting-code-review)<a href="https://agentmods.dev/skills/jessinra/lorekeeper/requesting-code-review"><img src="https://agentmods.dev/badge/skills/jessinra/lorekeeper/requesting-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/jessinra/lorekeeper/requesting-code-review"><img src="https://agentmods.dev/badge/skills/jessinra/lorekeeper/requesting-code-review.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 | $0.00020 | $0.01245 |
| Opus 5.5 | $0.00008 | $0.00498 |
| Sonnet 5.5 | $0.00004 | $0.00249 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
requesting-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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Commit Code Verification
Automated verification pipeline before code lands. Static scans, baseline-aware quality gates, an independent reviewer subagent, and an auto-fix loop.
Core principle: No agent should verify its own work. Fresh context finds what you miss.
When to Use
- After implementing a feature or bug fix, before
git commitorgit push - When user says "commit", "push", "ship", "done", "verify", or "review before merge"
- After completing a task with 2+ file edits in a git repo
- After each task in subagent-driven-development
Skip for: documentation-only changes, pure config tweaks, or when user says "skip verification".
This skill vs github-code-review: This skill verifies YOUR changes before committing. github-code-review reviews OTHER people's PRs on GitHub.
Step 1 — Get the diff
git diff --cached
If empty, try git diff then git diff HEAD~1 HEAD. If still empty, run git status.
If the diff exceeds 15,000 characters, split by file:
git diff --name-only
git diff HEAD -- specific_file.py
Step 2 — Static security scan
See references/scan-and-patterns.md for the full scan commands (hardcoded secrets, shell injection, eval/exec, unsafe deserialization, SQL injection).
Step 3 — Baseline tests and linting
See references/scan-and-patterns.md for test framework detection and linting commands (Python, Node, Rust, Go).
Baseline comparison: stash changes, run baseline, pop. Only NEW failures introduced by your changes block the commit.
Step 4 — Self-review checklist
- No hardcoded secrets, API keys, or credentials
- Input validation on user-provided data
- SQL queries use parameterized statements
- File operations validate paths (no traversal)
- External calls have error handling (try/catch)
- No debug print/console.log left behind
- No commented-out code
- New code has tests (if test suite exists)
Lorekeeper pre-flight (run before gh pr create)
What ships with it
2 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.
- 1mo ago First seen · 130 lines · 20 tokens per session scan A 6aef6606ccfc
requesting-code-review is a skill published in the GitHub repository Jessinra/Lorekeeper (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 20 tokens to every session and 1,245 once invoked, about $0.0001 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.
Other skills, from other repositories
pre-pr
Prepare a Honcho change for a pull request to plastic-labs/honcho. Invoke before opening a PR, when drafting a PR body, when asked if a branch is PR-ready, or when filling the pull request template. Checks the linked issue, required tests and docs, then writes Description / Proofs / Fixes.
review-comments
Execute a feature-level review-comments cycle — aggregate unaddressed PR comments across every Feature.Repos PR, address or dismiss each one, and emit the standard handoff.
review-implementation-functionality-evidence
Implementation review Functionality/Evidence axis - audits plan satisfaction and verification evidence.
review-implementation-craft
Implementation review Craft axis - audits intrinsic code quality only.
review-implementation-cleanliness
Implementation review Cleanliness axis - audits change-set hygiene and pushability.
final-fix
Final-review fix pass that addresses requested changes and refreshes invalidated evidence.