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/fcakyon/phd-skills/xraygit clone --depth 1 https://github.com/fcakyon/phd-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/commands/fcakyon/phd-skills/xray)<a href="https://agentmods.dev/commands/fcakyon/phd-skills/xray"><img src="https://agentmods.dev/badge/commands/fcakyon/phd-skills/xray.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 | $0.00032 | $0.00674 |
| Opus 5 | $0.00016 | $0.00337 |
| Sonnet 5 | $0.00006 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
xray 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 4d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper X-Ray Audit
You are performing a multi-dimensional audit of a research paper against its codebase and experimental results.
Step 1: Discover Paper Files
Find all .tex files in the project:
- Use Glob to find
**/*.texfiles - Identify the main .tex file (usually imports other sections)
- Find .bib files for citation checking
- Find result files (JSON, CSV, logs) for numerical verification
Step 2: Launch Parallel Audits
Spawn 5 sub-agents in parallel, each handling one audit dimension. Each agent should return a structured list of findings rated HIGH/MEDIUM/LOW.
Agent 1: Numerical Accuracy
Task: Extract every number from the .tex files (dataset sizes, metric values, percentages, counts). For each number, trace it to its source in the codebase — a result file, code output, config, or tracking system. Report any number that doesn't match its source or cannot be traced.
Agent 2: Terminology Consistency
Task: Extract all technical terms defined in the methods section. Search for each term across ALL sections of the paper. Flag inconsistent usage: same concept with different names, same name with different meanings, defined but unused terms, or used but undefined terms.
Agent 3: Code-Paper Alignment
Task: For each method or algorithm described in the paper, find the corresponding code implementation. Compare the paper's description with the actual code. Check that hyperparameters, architecture details, loss functions, and training procedures match between paper and code.
Agent 4: Citation Accuracy
Task: Read the .bib file. For each entry, verify author names, venue, and year against web search or DBLP. For cited claims in the .tex files that include specific numbers, check whether those numbers appear in the cited paper. Flag unverified metadata and unsupported cited claims.
Agent 5: Evaluation Integrity
Task: Review the evaluation code and result files. Check for: data leakage between train/val/test splits, off-by-one errors in metric computation, wrong aggregation methods (micro vs macro), mismatched ground-truth and prediction alignment, zero-division edge cases, and results that don't reproduce from the evaluation scripts.
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.
- 4d ago First seen · 64 lines · 32 tokens per session scan A 34e89961f991
xray is a command published in the GitHub repository fcakyon/phd-skills (382 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 674 once invoked, about $0.0002 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.
Other commands, from other repositories
brainstorm
Generate 10 ranked research ideas from a topic area using structured heuristic lenses.
idea
Screen a research idea with scoring and novelty check.
lit-review
Write a structured literature review on a topic with Corbis-backed searches and BibTeX citations.
verify-citations
Verify all citations against bibliography.
lit-landscape
Generate figures visualizing literature trends, citation patterns, and research gaps.
lit-search
Search the literature and build a positioning memo.