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 commands/insight-services-apac/ingenious/large-filesgit clone --depth 1 https://github.com/Insight-Services-APAC/ingeniousWrote 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/commands/insight-services-apac/ingenious/large-files)<a href="https://agentmods.dev/commands/insight-services-apac/ingenious/large-files"><img src="https://agentmods.dev/badge/commands/insight-services-apac/ingenious/large-files.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.00246 |
| Opus 5 | $0.00000 | $0.00123 |
| Sonnet 5 | $0.00000 | $0.00049 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
large-files 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.
What it actually says
Identify and split large files across the codebase:
-
Identify candidates (any of these criteria):
- Files >500 lines
- Files with >5 distinct classes/functions handling different concerns
- Files with multiple unrelated responsibilities
- Files that violate Single Responsibility Principle
-
Split strategy:
- Separate by domain/functionality (models, services, utils, validators)
- Group related classes/functions into cohesive modules
- Extract common utilities to shared modules
- Create feature-based or component-based organization where appropriate
-
File organization:
- Create logical module structure with clear naming conventions
- Update imports across codebase
- Maintain backward compatibility with existing imports where possible
- Add module index files (e.g.,
__init__.py,index.ts) for clean public APIs
-
Quality checks:
- Ensure tests still pass after refactoring
- Verify no circular dependency issues
- Check that each new file has clear, single purpose
- Update documentation/comments as needed
- Run linters and formatters to maintain code style
Prioritize files causing the most maintenance friction or merge conflicts.
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 · 29 lines · 0 tokens per session scan A cd14f5ba57f8
large-files is a command published in the GitHub repository Insight-Services-APAC/ingenious (24 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 246 tokens. 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 2026-09-04.
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