Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Authors
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

ProTermsPrivacyRefunds
Back to skills

Raw

ASecurity

Subject: Action Plan to Reduce Composing Costs and Restore Capacity Composition accounts for 40–55% of our production costs, making it our largest single expense. Yet our manual process is currently overloaded, driving missed deadlines, overtime costs over budget by 50%, and staff turnover, while industry benchmarks show we trail competitors by 20–50% and remain uncompetitive on simple work. To cut these costs and restore reliable delivery, we should simplify quality checks for low-complexity...

2 stars
0 votes
0 copies
1 views
Added 9/19/2026
ai-agentstesting

Security Analysis

A100/100

Scanned 9/19/2026

Install to Claude Code

$npx -y skills add welltraum/minto --skill raw --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Raw?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for Raw
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/welltraum-raw-151aca1c/badge)](https://www.skillsdirectory.com/skills/welltraum-raw-151aca1c)

More formats (shields.io, HTML) on the badges page.

Download with Pro
Files
01-big-chief__codex__control.md
Subject: Action Plan to Reduce Composing Costs and Restore Capacity

Composition accounts for 40–55% of our production costs, making it our largest single expense. Yet our manual process is currently overloaded, driving missed deadlines, overtime costs over budget by 50%, and staff turnover, while industry benchmarks show we trail competitors by 20–50% and remain uncompetitive on simple work. To cut these costs and restore reliable delivery, we should simplify quality checks for low-complexity titles and shift to automated composition methods.

1. Pilot simplified quality checks on low-complexity titles
   - Every title, from complex reference books to simple novels, currently passes through identical quality-control stages.
   - Targeted testing of selected jobs can measure the impact of removing or rescheduling specific checks.
   - Projected savings from this streamlining could reach 10% of total composing costs.

2. Commission a methods study to transition to automated composition
   - Manual composition is severely capacity-constrained and lags industry benchmarks by 20–50%.
   - Changing the core composing method addresses the root productivity gap rather than merely managing existing bottlenecks.
   - A dedicated study will identify specific automation opportunities and define the implementation pathway.

3. Validate findings through internal pilots and external benchmarking
   - Key team members (Roy Walter, Brian Thompson, and George Kennedy) are available to run initial tests and assess stage removal.
   - Comparing our metrics against three peer printers will provide essential context, though standalone comparisons alone will not resolve the underlying inefficiencies.

Material omitted or demoted: Detailed staffing grievances (below-market pay, recent departures, union claims) were moved to background context as they describe current operational pressures rather than direct solutions. The observation that managers disagree on whether costs are excessive was removed, as the investigation data and benchmark gaps already establish the necessity of action.

Attribution

welltraumwelltraum
View sourceMore from welltraum →
SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Related Skills

Caveman

Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".

1066601 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

686011 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3351 votes

catchup

Recovers the conversation and failed tool calls of a previous Codex, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or report a catchup failure. Do NOT use for the current conversation, git history, or any non-agent log.

651 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →