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/jamestorrevillas/dev-skills/code-readingnpx skills add jamestorrevillas/dev-skills --skill code-readinggit clone --depth 1 https://github.com/jamestorrevillas/dev-skillsWhat 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 | $0.00066 | $0.00625 |
| Opus 5 | $0.00033 | $0.00313 |
| Sonnet 5 | $0.00013 | $0.00125 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
code-reading 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reading
Core Principle
Code is read 10x more than it's written. Reading code is a skill you actively develop — not a passive activity.
Codebase Onboarding Order
Always explore a new codebase in this order:
- README — What does it do? How do I run it?
- Package/dependency files — What tools and frameworks are used?
- Entry point — Where does execution start? (main.ts, index.js, app.py)
- Core domain models — What are the main data structures?
- Key user flows — Trace one important feature end-to-end
- Tests — Tests reveal intended behavior and edge cases
- Config/env files — What is configurable? What are the environments?
Code Tracing Method
For understanding a specific flow:
1. Start from the trigger (user action, API call, scheduled job)
2. Follow the execution path step by step
3. Note: what data flows in? what comes out?
4. Identify where external systems are called
5. Mark where business logic lives vs. infrastructure
Questions to Ask While Reading
- What is this component responsible for? (single responsibility check)
- What does it need to run? (dependencies)
- What does it produce/return?
- What could make it fail?
- Why was it written this way? (check git blame/history for context)
Using AI for Code Reading
"Explain what this function does, including:
- What it takes as input
- What it returns
- Any side effects
- Non-obvious behavior I should know about"
"Walk me through how [feature] works,
starting from [entry point] to [output]"
"What would I need to understand to safely
modify [specific part] of this code?"
Navigating Large Codebases
- Search patterns — use grep/ripgrep to find usages of a function/class
- Git blame — who changed this and why?
- Git log — when was this introduced? what changed over time?
- Tests — run tests while reading to see what behavior is expected
- Dependency graph — which modules depend on what?
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.
- 2d ago First seen · 84 lines · 0 tokens per session scan A 27073639bf70
code-reading is a skill published in the GitHub repository jamestorrevillas/dev-skills (3 stars, last pushed 5mo ago), licensed MIT. It adds 66 tokens to every session and 625 once invoked, about $0.0003 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.
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