fail_name

A test fixture designed to fail a check that validates a skill's frontmatter name. Frontmatter is the metadata at the start of a skill file, and this fixture uses an intentionally incorrect name.

In plain words
What is it for?
Use it when testing frontmatter validation, especially the rule that requires the name to match the directory with a "plastic-" prefix.
Why use it?
It provides a controlled case for verifying that name validation catches a mismatch between the declared name and the skill directory name.

Skill for Claude CodeCodex

Part of the plastic plugin — 43 skills, 10 agents, 5 hooks, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add skills/zalom/plastic/fail_name
Any agent
npx skills add zalom/plastic --skill fail_name
Clone the repo
git clone --depth 1 https://github.com/zalom/plastic

Made for: Claude Code, Codex.

Or install plastic, the plugin that ships this one along with the rest of its 43 skills, 10 agents, 5 hooks, 1 MCP server.

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 94 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.00094
Opus 5 $0.00012 $0.00047
Sonnet 5 $0.00005 $0.00019
Haiku 4.5 $0.00002 $0.00009

Measured 3d ago against content hash f3a4aeb1f559, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fail_name 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 3d 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.

test/fixtures/skill_lint/fail_name/SKILL.md · 12 lines

What it actually says

Fail Name Fixture

The frontmatter name: above is fail_name, the bare directory name, lacking the required plastic- prefix. The corrected rule requires name == "plastic-#{directory}", i.e. plastic-fail_name here.

Changes

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.

  1. 3d ago First seen · 12 lines · 23 tokens per session scan A f3a4aeb1f559

Subscribe to this mod's changes

fail_name is a skill published in the GitHub repository zalom/plastic (10 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 94 once invoked, about $0.0001 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 2026-08-31.

Related

Other skills, from other repositories

rulesync

Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.

dyoshikawa/rulesync · 64 tokens

agent-workspace-linux

Use when a task needs an isolated hidden Linux desktop or workspace-owned browser: GUI app QA, web/browser/shopping automation, sandboxed app observation, or stale workspace cleanup. Routes agent-workspace-linux MCP tools on demand. Does NOT apply to host desktop/Chrome control, generic MCP setup, or pure code/file…

ilysenko/codex-desktop-linux · 70 tokens

ss-component

Generate a new UI component following the StyleSeed design conventions.

bitjaru/styleseed · 14 tokens

loongsuite-pilot-insight

基于 LoongSuite Pilot / AI Coding Agent 日志生成事件洞察、组织洞察、数据质量、研发效能和 AI Native 使用类 SLS 报表时使用;包含 AI Coding 事件表语义,以及团队报表可选的部门维表、deptuser 组织关系、指标口径和公共 CTE,通常与 sls-dashboard-builder 一起使用。.

alibaba/loongsuite-pilot · 91 tokens

map-review

Interactive 4-section code review using monitor, predictor, and evaluator agents plus the user and maintainer role reviewers on current changes. Use when reviewing a diff, PR, or staged work before merge. Do NOT use to plan or implement; use map-plan or map-efficient.

azalio/map-framework · 58 tokens

map-fast

Minimal workflow for small, low-risk changes — no planning, no learning.

azalio/map-framework · 17 tokens