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 agentmods add skills/lgbarn/shipyard/code-simplificationnpx skills add lgbarn/shipyard --skill code-simplificationgit clone --depth 1 https://github.com/lgbarn/shipyardWhat 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 | $0.00086 | $0.02841 |
| Opus 5 | $0.00043 | $0.01421 |
| Sonnet 5 | $0.00017 | $0.00568 |
| Haiku 4.5 | $0.00009 | $0.00284 |
Grade A, and why
code-simplification 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 yesterday.
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 — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Simplification
When This Skill Activates
- After all tasks in a phase are complete (before shipping)
- When reviewing code generated by multiple builder agents
- When a file has been touched by 3+ different tasks
- When you notice patterns repeating across files
- Before claiming a phase is production-ready
The simplifier agent references this skill for systematic cross-task analysis.
Natural Language Triggers
- "simplify this", "clean up", "too complex", "reduce complexity", "this is bloated"
Overview
AI-generated code accumulates complexity. Each task is implemented in isolation by a fresh agent that can't see the full picture. After multiple tasks, duplication creeps in, abstractions multiply, and dead code lingers.
Core principle: The simplest code that works correctly is the best code. Complexity is a cost, not a feature.
This skill applies after implementation, not during. Don't prematurely optimize -- but don't ship bloat either.
Simplification Process
When reviewing code for simplification:
- Identify scope: What files changed in this phase? (Use git diff)
- Scan for duplication: Look for similar patterns across files
- Check complexity: Flag functions exceeding thresholds
- Find dead code: Look for unused definitions
- Spot over-engineering: Look for abstractions with single implementations
- Check AI patterns: Apply the AI anti-pattern checklist
- Prioritize findings:
- High: Clear duplication (3+), dead code, obvious bloat
- Medium: Complexity reduction, near-duplicates
- Low: Style consistency, minor simplifications
Duplication Detection
What to Look For
Exact duplicates: Identical code blocks in different files or functions.
# RED FLAG: Same logic in two places
def validate_user_email(email):
if not email or "@" not in email:
raise ValueError("Invalid email")
def validate_contact_email(email):
if not email or "@" not in email:
raise ValueError("Invalid email")
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.
- yesterday First seen · 410 lines · 86 tokens per session scan A 76ab193ee929
code-simplification is a skill published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 2,841 once invoked, about $0.0004 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.
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