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 ThibautBaissac/rails_ai_agents --skill friction-reviewgit clone --depth 1 https://github.com/ThibautBaissac/rails_ai_agentsWrote 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/thibautbaissac/rails_ai_agents/friction-review)<a href="https://agentmods.dev/skills/thibautbaissac/rails_ai_agents/friction-review"><img src="https://agentmods.dev/badge/skills/thibautbaissac/rails_ai_agents/friction-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/thibautbaissac/rails_ai_agents/friction-review"><img src="https://agentmods.dev/badge/skills/thibautbaissac/rails_ai_agents/friction-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 13 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00128 | $0.01555 |
| Opus 5 | $0.00064 | $0.00777 |
| Sonnet 5 | $0.00026 | $0.00311 |
| Haiku 4.5 | $0.00013 | $0.00155 |
Grade A, and why
friction-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 11d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Friction Review
ultrathink
You are the friction orchestrator. Route an artifact through 5 specialized reviewer subagents, collect their independent assessments, and produce a consolidated friction report for human arbitration.
Artifact: $ARGUMENTS
If $ARGUMENTS is a file path, read it now and hold the full content in context. If it is a description, use it as-is. If no argument is given, ask the user what to review before proceeding.
Friction Marker Taxonomy
Every reviewer must use exactly these four tags:
[sound]— well-founded, no objection on this axis[contestable]— valid but alternatives exist; explain briefly[blind_spot]— something the artifact doesn't address that it should[refuted]— a mistake on this axis; explain why and what breaks
Step 1 — Read the Artifact
Read the artifact in full. If it imports or references other files (schema, service, config), read those too. Build complete context before spawning.
Step 2 — Spawn All 5 Reviewers in Parallel
Use the Agent tool to launch all 5 reviewer subagents simultaneously in a single message. Pass the full artifact content in each prompt. Do not wait for one to finish before spawning the next.
Each subagent should use tools: Read, Grep, Glob (read-only). Each returns 5–10 bulleted findings tagged with the friction markers above.
Subagent 1 — Architecture Reviewer
Spawn a general-purpose subagent with this prompt (substitute ARTIFACT_CONTENT with the actual artifact text you read in Step 1):
You are the Architecture Reviewer. Axis: system boundaries, layer separation, abstractions, SOLID principles, data flow, service contracts, naming.
Prohibitions: Do not suggest implementation code. Do not comment on test coverage. Do not make product decisions. Do not propose DB schema choices.
Review the artifact and return 5–10 bulleted findings tagged [sound], [contestable], [blind_spot], or [refuted]. Name the concept, cite the location in the artifact, and explain why.
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.
- 11d ago First seen · 206 lines · 128 tokens per session scan A 9921dc8ae93f
friction-review is a skill published in the GitHub repository ThibautBaissac/rails_ai_agents (661 stars, last pushed 3mo ago), licensed MIT. It adds 128 tokens to every session and 1,555 once invoked, about $0.0006 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
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…