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 skills add jamestorrevillas/dev-skills --skill code-readinggit clone --depth 1 https://github.com/jamestorrevillas/dev-skillsWrote 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/skills/jamestorrevillas/dev-skills/code-reading)<a href="https://agentmods.dev/skills/jamestorrevillas/dev-skills/code-reading"><img src="https://agentmods.dev/badge/skills/jamestorrevillas/dev-skills/code-reading/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jamestorrevillas/dev-skills/code-reading"><img src="https://agentmods.dev/badge/skills/jamestorrevillas/dev-skills/code-reading.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00066 | $0.00625 |
| Opus 5.5 | $0.00026 | $0.00250 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 7mo ago), licensed MIT. It adds 66 tokens to every session and 625 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
performance-profiling
CPU, memory, network bottleneck analysis — systematic performance investigation.
refactor
Systematic code refactoring to improve maintainability without changing behavior. Use when asked to "refactor this", "clean up this code", "improve code quality", "simplify this", "reduce complexity", or "eliminate code smells". Always preserves existing behavior.
power-bi-performance-troubleshooting
Systematic Power BI performance troubleshooting prompt for identifying, diagnosing, and resolving performance issues in Power BI models, reports, and queries.
refactoring-patterns
Safe transformations — same behavior, better structure.
extension-audit-methodology
Systematic 5-dimension audit framework for VS Code extensions — debug hygiene, dead code, performance, menu validation, dependency cleanup.
universal-audit-pattern
Systematic project audit pattern — version consistency, terminology, fact inventory, cross-references.