Coral is a local application that gives AI agents one SQL interface for querying connected APIs, files, and live data sources. It is for agents and developers who need to retrieve and correlate data across sources while reducing repeated tool calls, pagination, and source-specific integration code. Coral exposes its query interface to compatible agents through MCP, and the catalogue add-ons support using it in agent workflows.
Borrowing it
Nothing to install: this file belongs to withcoral/coral. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/withcoral/coral/main/.agents/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/withcoral/coralWrote 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/withcoral/coral/code-review)<a href="https://agentmods.dev/skills/withcoral/coral/code-review"><img src="https://agentmods.dev/badge/skills/withcoral/coral/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/withcoral/coral/code-review"><img src="https://agentmods.dev/badge/skills/withcoral/coral/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 | $0.00094 | $0.01521 |
| Opus 5.5 | $0.00038 | $0.00608 |
| Sonnet 5.5 | $0.00019 | $0.00304 |
| Haiku 4.5 | $0.00009 | $0.00152 |
Grade A, and why
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 8d 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.
This is a copy
88% identical to code-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Two-axis review of the diff between HEAD and a fixed point the user supplies:
- Standards: does the code conform to this repo's documented coding standards?
- Spec: does the code faithfully implement the originating issue / spec?
Both axes run as parallel sub-agents so they don't pollute each other's context, then this skill aggregates their findings.
The issue tracker should have been provided to you. If not, tell the user to run /setup-matt-pocock-skills.
Process
1. Pin the fixed point
Whatever the user said is the fixed point (a commit SHA, branch name, tag, main, HEAD~5, etc.). If they didn't specify one, ask for it.
Capture the diff command once: git diff <fixed-point>...HEAD (three-dot, so the comparison is against the merge-base). Also note the list of commits via git log <fixed-point>..HEAD --oneline.
Before going further, confirm the fixed point resolves (git rev-parse <fixed-point>) and the diff is non-empty. A bad ref or empty diff should fail here, not inside two parallel sub-agents.
2. Identify the spec source
Look for the originating spec, in this order:
- Issue references in the commit messages (
#123,Closes #45, GitLab!67, etc.), fetched via the workflow in the tracker doc. - A path the user passed as an argument.
- A spec file under
docs/,specs/, or.scratch/matching the branch name or feature. - If nothing is found, ask the user where the spec is. If they say there isn't one, the Spec sub-agent will skip and report "no spec available".
3. Identify the standards sources
Search the repo for every file that documents how code should be written. When CODING_STANDARDS.md or CONTRIBUTING.md exists, it must be on the list.
On top of whatever the repo documents, the Standards axis always carries the smell baseline below: a fixed set of Fowler code smells (Refactoring, ch.3) that applies even when a repo documents nothing. Two rules bind it:
- The repo overrides. A documented repo standard always wins; where it endorses something the baseline would flag, suppress the smell.
- Always a judgement call. Each smell is a labelled heuristic ("possible Feature Envy"), never a hard violation. Like any standard here, skip anything tooling already enforces.
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.
- 8d ago First seen · 90 lines · 94 tokens per session scan A 09eb147f793e
code-review is a skill published in the GitHub repository withcoral/coral (4,938 stars, last pushed today), licensed Apache-2.0. It adds 94 tokens to every session and 1,521 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to code-review, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
pr
Draft a PR body from OMC's paper trail — the smallest visual that proves the change, evidence consumed from the verify protocol (never re-collected), reversibility from the ADR test on the diff, glossary language throughout.
merge-batch
Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.
otto-pr-review-loop
Run Polly and OCR on a PR until all findings settle.
engram-branch-pr
PR creation workflow for Engram following the issue-first enforcement system. Trigger: When creating a pull request, opening a PR, or preparing changes for review.
github-contributor
A step-by-step guide for contributing code, tests, documentation, or build changes to open-source projects you do not maintain. It explains how to prepare a pull request that follows the project’s rules and is easier for maintainers to review.
revdiff
Review diffs, files, and documents with inline annotations in a TUI overlay, or answer questions about revdiff usage, configuration, themes, and keybindings. Opens revdiff in agterm/tmux/zellij/herdr/kitty/wezterm/cmux/ghostty/iterm2/emacs-vterm, captures annotations, and addresses them. Works in git, hg, and jj repos…