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 HigorAlves/orc --skill inline-reviewgit clone --depth 1 https://github.com/HigorAlves/orcWrote 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/higoralves/orc/inline-review)<a href="https://agentmods.dev/skills/higoralves/orc/inline-review"><img src="https://agentmods.dev/badge/skills/higoralves/orc/inline-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/higoralves/orc/inline-review"><img src="https://agentmods.dev/badge/skills/higoralves/orc/inline-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 | $0.00044 | $0.01388 |
| Opus 5.5 | $0.00018 | $0.00555 |
| Sonnet 5.5 | $0.00009 | $0.00278 |
| Haiku 4.5 | $0.00004 | $0.00139 |
Grade A, and why
inline-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 4d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inline GitHub PR Review
Convert a list of structured findings (from orc-pr-reviewer and/or orc-security-reviewer) into a real GitHub PR review — inline comments anchored to specific files and lines, optional one-click apply suggestion blocks, and a review event (APPROVE / COMMENT / REQUEST_CHANGES) computed mechanically from finding severities.
This skill replaces the legacy text-block summary format. The reviewed author opens the PR and sees comments pinned next to the actual code, not a markdown wall they have to manually navigate.
When to use
- Inside
/orc:code-reviewPhase 5–7 after merging findings from agents. - Anywhere else you need to post a structured PR review programmatically.
When NOT to use
- For top-level PR comments without line anchors → use
gh pr comment. - For replying to an existing review comment → use the replies endpoint (
gh api repos/.../pulls/.../comments/<id>/replies); seeorc:receiving-code-review. - For your OWN PR's response loop → use
/orc:address, not this skill.
The contract (schema, severities, event rule)
The finding schema, severity enum, severity→event mapping, event-computation
algorithm, confidence ≥0.8 rule, and self-contradiction detection are defined
once, in orc:review-contract. Read that skill before posting; this skill
owns only the posting mechanics below.
Posting-layer obligations from the contract, restated as duties (not schema):
- Compute the event mechanically from the severity set — the agent's narrative
verdict is ignored.
--soft-testsrelaxes only thetestseverity. - Run self-contradiction detection on each agent
summary; warn with a> **⚠️ Caution**callout, then post the computed event anyway. - Validate each finding's
path/lineper the contract's line-number semantics before posting.
Posting backend selection
Two paths produce identical end-result on GitHub. The orchestrator picks at runtime: gh api repos/…/pulls/…/reviews (one atomic POST — default) or the GitHub MCP 3-call protocol when mcp__plugin_github_github__pull_request_review_write is available. Read BACKENDS.md for the payload shape, the MCP call sequence, and the selection logic.
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.
- 4d ago Changed · -128 lines 8a86e1f9fb6d
- 19d ago Changed aed5abffae51
- 1mo ago First seen · 214 lines · 44 tokens per session scan A fb887bb782f0
inline-review is a skill published in the GitHub repository HigorAlves/orc (7 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,388 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 2026-09-03.
Other skills, from other repositories
pr-triage
4-phase PR backlog management with audit, deep code review, validated comments, and optional worktree setup. Use when triaging pull requests, catching up on pending code reviews, or managing a backlog of open PRs. Args: 'all' to review all, PR numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit…
review-pr
Perform a comprehensive code review of a pull request.
pr
Analyze changes, detect scope issues, and create a well-structured PR.
validate-changes
Evaluate staged changes using LLM-as-a-Judge before committing.
deploy-landed
A deployment verification procedure that checks whether the intended code revision is actually running, its environment settings are present, and the product works. A deployment is the process of putting new software on a server or hosting platform.
signing-preflight
Before the first commit of a session, check that commit signing will work — the key is loaded and unlocked — and ask the operator to unlock it once, up front, instead of discovering it after a hundred unsigned commits. Never disables signing silently, never re-signs history without asking. Use at the start of…