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Agent skill

Friction Review

by ThibautBaissac in ThibautBaissac/rails_ai_agents

Multi-axis adversarial review using friction engineering. An agent skill from ThibautBaissac/rails_ai_agents.

MITAuto-check: notesDevelopment

Install Friction Review

skills CLI
$ npx skills add ThibautBaissac/rails_ai_agents --skill friction-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ThibautBaissac/rails_ai_agents friction-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ThibautBaissac/rails_ai_agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/friction-review .claude/skills/friction-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
friction-review
GitHub stars
665
Token cost
~1.7k tokens
SKILL.md length
807 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Multi-axis adversarial review using friction engineering. An agent skill from ThibautBaissac/rails_ai_agents.

  • Works in 3 steps: Read the Artifact → Spawn All 5 Reviewers in Parallel → Consolidate into a Friction Report
  • Reviewing feature specs
  • SKILL.md covers Friction Marker Taxonomy, Step 1 — Read the Artifact, Step 2 — Spawn All 5 Reviewers… and Step 3 — Consolidate into a…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Friction Review is an agent skill from ThibautBaissac/rails_ai_agents. Multi-axis adversarial review using friction engineering. Routes a spec, plan, ADR, service design, schema, or any design artifact through 5 specialized reviewers with explicit prohibitions. Each reviewer tags positions as [sound], [contestable], [blindspot], or [refuted]. Returns a consolidated friction report for human arbitration before implementation begins. Use when reviewing feature specs, architecture decisions, service designs, database schemas, or any artifact where hidden assumptions must be surfaced…

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Architecture decision records, Dispute resolution and Database schema design. The repository describes itself as: Specialized AI skills, agents, rules and hooks for modern Rails AI driven-development + Spec-Driven-Development kit + MCP. The licence is MIT.

When your agent uses it

  • Reviewing feature specs
  • Architecture decisions
  • Service designs
  • Database schemas

Example prompts

  • “/friction-review”

Requirements

  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash, Agent

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Read the Artifact
  2. Spawn All 5 Reviewers in Parallel
  3. Consolidate into a Friction Report

What it can do on your machine

Read from SKILL.md and the folder at commit 03622f2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash
    • Agent

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Friction Review loads about 1.7k tokens when it runs. Until then it costs about 170 tokens; SKILL.md has 807 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~170
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash, Agent

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ThibautBaissac/rails_ai_agents at commit 03622f2, republished under its MIT licence (© ThibautBaissac). 807 words, ~1,722 tokens.

Download SKILL.mdSave it as .claude/skills/friction-review/SKILL.md (or your agent's skills folder).
name
friction-review
description
Multi-axis adversarial review using friction engineering. Routes a spec, plan, ADR, service design, schema, or any design artifact through 5 specialized reviewers with explicit prohibitions. Each reviewer tags positions as [sound], [contestable], [blind_spot], or [refuted]. Returns a consolidated friction report for human arbitration before implementation begins. Use when reviewing feature specs, architecture decisions, service designs, database schemas, or any artifact where hidden assumptions must be surfaced. WHEN NOT: code already written (use code-review), routine CRUD with no design decisions, quick one-off questions, or post-implementation reviews.
allowed-tools
Read, Grep, Glob, Bash, Agent
model
opus
effort
high
user-invocable
true
argument-hint
[file path or description of artifact to review]

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.

ARTIFACT: ARTIFACT_CONTENT

Subagent 2 — Implementation Reviewer

Spawn a general-purpose subagent with this prompt:

You are the Implementation Reviewer. Axis: code patterns, technical feasibility, gem choices, ActiveRecord patterns, N+1 risks, data types, callback usage, service object design, TypeScript/Rails conventions.

Prohibitions: Do not make architectural decisions (layer boundaries, abstractions). Do not approve or reject product features. Do not comment on security vulnerabilities. Do not propose DB schema changes.

Review the artifact and return 5–10 bulleted findings tagged [sound], [contestable], [blind_spot], or [refuted]. Name the pattern, cite the relevant section, and explain why.

ARTIFACT: ARTIFACT_CONTENT

Subagent 3 — Testability Reviewer

Spawn a general-purpose subagent with this prompt:

You are the Testability Reviewer. Axis: test coverage gaps, edge cases, factory complexity, isolation difficulty, hard-to-test paths, missing acceptance criteria, unclear preconditions.

Prohibitions: Do not suggest code structure changes. Do not make architectural decisions. Do not propose implementation patterns. Do not comment on security.

Review the artifact and return 5–10 bulleted findings tagged [sound], [contestable], [blind_spot], or [refuted]. Name the test scenario and explain what makes it hard or missing.

ARTIFACT: ARTIFACT_CONTENT

Show full SKILL.md (339 more words)Show less
Subagent 4 — Security Reviewer

Spawn a general-purpose subagent with this prompt:

You are the Security Reviewer. Axis: authentication, authorization, OWASP Top 10, data exposure, input validation, mass assignment, SQL/prompt injection, XSS, sensitive data handling, audit logging gaps.

Prohibitions: Do not suggest feature changes. Do not comment on code style or conventions. Do not propose architectural patterns. Do not comment on testability.

Review the artifact and return 5–10 bulleted findings tagged [sound], [contestable], [blind_spot], or [refuted]. Name the vulnerability class, cite the section, and explain the attack vector.

ARTIFACT: ARTIFACT_CONTENT

Subagent 5 — Simplicity Reviewer

Spawn a general-purpose subagent with this prompt:

You are the Simplicity Reviewer (YAGNI/KISS enforcer). Axis: premature abstractions, unnecessary complexity, over-engineering, scope creep, things that could be simpler without loss of correctness.

Prohibitions: Phrase ALL findings as questions, never prescriptions ("Why does X need Y?" not "Remove Y"). Do not propose alternatives. Do not approve or reject architectural patterns. Do not comment on security or testing.

Review the artifact and return 5–10 bulleted findings phrased as questions, tagged [sound], [contestable], [blind_spot], or [refuted]. Be direct: name the assumption being questioned and why.

ARTIFACT: ARTIFACT_CONTENT

Step 3 — Consolidate into a Friction Report

After all 5 subagents return, compile their findings into this exact structure:

Friction Report: [artifact name or path]

Architecture Axis
  • [tag] finding...
Implementation Axis
  • [tag] finding...
Testability Axis
  • [tag] finding...
Security Axis
  • [tag] finding...
Simplicity Axis
  • [tag] finding...

Arbitration Required

Positions requiring your decision (contested across axes, or critical blind spots):

  1. [topic]: [axis A position] vs [axis B position] → Your call: ___
  2. ...

Ratified Positions

[sound] across 3+ axes — proceed with confidence:

  • ...

Next Step

Resolve the arbitration items above, then proceed to implementation. Ratified positions are constraints the implementation must respect.

Orchestrator Rules

  • Do not resolve arbitration yourself. Surface conflicts, do not merge them.
  • Do not skip reviewers. A missing axis defeats the method.
  • Do not summarize friction away. Report it verbatim from subagents.
  • If a reviewer axis is silent (no findings), note it explicitly.
  • If the artifact is too vague, stop and ask before spawning.

© ThibautBaissac, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/friction-review of ThibautBaissac/rails_ai_agents.

Open the folder on GitHubat commit 03622f2

Compare with similar skills

Friction Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Friction Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Friction Review this skillThibautBaissac/rails_ai_agents665—~1.7kAutomated safety check: NotesMIT
Ad ReviewCorridorTech/PoseCap224—~2.4kAutomated safety check: NotesApache-2.0
Walkthroughalexanderop/walkthrough141—~3.4kAutomated safety check: NotesNone
Light System DesignLight0305/Light-skills640—~3.6kAutomated safety check: PassMIT
Cabloy Spec Generationcabloy/cabloy982—~3.2kAutomated safety check: NotesMIT
Bmad Architectureaj-geddes/claude-code-bmad-skills488—~1.9kAutomated safety check: NotesCustom licence

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Categories

Questions about Friction Review

What does Friction Review do?

Multi-axis adversarial review using friction engineering. An agent skill from ThibautBaissac/rails_ai_agents. Friction Review is an agent skill from ThibautBaissac/rails_ai_agents. Multi-axis adversarial review using friction engineering.

When should I use Friction Review?

Friction Review fits situations like: reviewing feature specs; architecture decisions; service designs; database schemas.

How do I install Friction Review in Claude Code?

Run `npx skills add ThibautBaissac/rails_ai_agents --skill friction-review -a claude-code`. Or copy the skill folder (.agents/skills/friction-review in ThibautBaissac/rails_ai_agents) into .claude/skills/friction-review in your project. Claude Code loads it when a task matches its description.

How do I install Friction Review in Codex?

Run `npx skills add ThibautBaissac/rails_ai_agents --skill friction-review -a codex`. Or copy the skill folder (.agents/skills/friction-review in ThibautBaissac/rails_ai_agents) into .agents/skills/friction-review in your project. Codex loads it when a task matches its description.

Can I use Friction Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ThibautBaissac/rails_ai_agents --skill friction-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/friction-review, .gemini/skills/friction-review, .github/skills/friction-review and .opencode/skills/friction-review in your project.

What does Friction Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Friction Review is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash, Agent.

Does Friction Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Friction Review safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Friction Review use?

Friction Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Friction Review use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Friction Review?

Skills that share tags, products or a category with Friction Review: Ad Review (CorridorTech/PoseCap, 224 stars), Walkthrough (alexanderop/walkthrough, 141 stars), Light System Design (Light0305/Light-skills, 640 stars) and Cabloy Spec Generation (cabloy/cabloy, 982 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Friction Review?

ThibautBaissac (a GitHub user) maintains it in ThibautBaissac/rails_ai_agents, which has 665 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on June 1, 2026.

Source: ThibautBaissac/rails_ai_agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.