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.
git clone --depth 1 https://github.com/letitbk/claude-academic-setupWrote 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/letitbk/claude-academic-setup/analyze-function)<a href="https://agentmods.dev/commands/letitbk/claude-academic-setup/analyze-function"><img src="https://agentmods.dev/badge/commands/letitbk/claude-academic-setup/analyze-function/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/commands/letitbk/claude-academic-setup/analyze-function"><img src="https://agentmods.dev/badge/commands/letitbk/claude-academic-setup/analyze-function.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 | $0.00000 | $0.01773 |
| Opus 5.5 | $0.00000 | $0.00709 |
| Sonnet 5.5 | $0.00000 | $0.00355 |
| Haiku 4.5 | $0.00000 | $0.00177 |
Grade A, and why
analyze-function 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze the specified function from the given file with detailed line-by-line breakdown.
Usage: /project:analyze-function filename:function_name or /project:analyze-function filename function_name
Arguments:
filename: The file containing the function to analyzefunction_name: The name of the function to analyze
For the function $ARGUMENTS, I will:
- Read and locate the function in the specified file
- Provide context about the function's role in the system
- Analyze each line explaining:
- What the code does technically
- Why it's implemented this way
- Performance implications
- Edge cases and potential issues
- Connection to the broader codebase
- Highlight critical details that might be missed from casual reading
- Explain design patterns and optimization techniques used
- Identify potential improvements or areas of concern
The analysis will focus on both the immediate functionality and the broader architectural context, helping you understand not just what the code does, but why it was written this way and what makes it effective (or potentially problematic) in its specific use case.
Example Analysis
Usage: /project:analyze-function train.py:detect_words_gpu
Context & Purpose
This function is part of the CurriculumManager class in a sophisticated GRPO (Group Relative Policy Optimization) training system. It's designed to efficiently detect and count words of different lengths in character-level sequences entirely on GPU to support curriculum learning stages that reward proper word formation.
Line-by-Line Analysis
Line 1326: Function Signature
def detect_words_gpu(self, sequences: torch.Tensor) -> torch.Tensor:
- What: Defines a method taking character index sequences and returning word counts
- Why: The
_gpusuffix indicates this is optimized for GPU execution, crucial since this runs in the training loop where CPU-GPU transfers would be catastrophic for performance - Context: Part of curriculum learning where later stages reward proper word formation
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 · 146 lines · 0 tokens per session scan A 024a6abfd9fd
analyze-function is a command published in the GitHub repository letitbk/claude-academic-setup (46 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,773 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 2026-08-30.
Other commands, from other repositories
qa-changes
This skill should be used when the user asks to "QA a pull request", "test PR changes", "verify a PR works", "functionally test changes", or when an automated workflow triggers QA validation of code changes. Provides a structured methodology for setting up the environment, exercising changed behavior, and reporting…
react-native
Command "react-native" from getsentry/toolkit, covering examples, also associate with a release and distribution(s), xcode build step (react-native xcode) and important notes.
doctor
Diagnosticar y reparar problemas del framework Don Cheli, git y entorno. Usa cuando el usuario dice "doctor", "problemas del framework", "don cheli no funciona", "repair Don Cheli", "debug setup", "setup broken", "framework broken", "reparar entorno". Detecta y repara issues de configuración, git y dependencias…
doctor
Badi configuration validation. Checks all Badi components and produces a diagnostic report.
auditar-cascading
Invoca cascading-failures-auditor — audita serviço para 5 triggers de cascade (sem timeout, retry sem jitter, sem circuit breaker, sem deadline, queue unbounded) e gera CASCADING-AUDIT.md.
http-service
Build, review or debug a Bun HTTP service. Loads the http-service skill, then works the task through its workflow.