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/codebytes/agent-skills/csv-analysisnpx skills add codebytes/agent-skills --skill csv-analysisgit clone --depth 1 https://github.com/codebytes/agent-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 | $0.00017 | $0.00417 |
| Opus 5 | $0.00009 | $0.00209 |
| Sonnet 5 | $0.00003 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
csv-analysis 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
Instructions
When asked to analyze a CSV file, follow this workflow:
Step 1: Read and Profile
- Read the first 50 lines of the CSV
- Identify the delimiter (comma, tab, semicolon, pipe)
- Count total rows and columns
- Infer column data types (string, integer, float, date, boolean)
Step 2: Compute Statistics
Run analysis to compute per-column statistics:
- Count, nulls, unique values
- Min, max, mean, median, standard deviation for numeric columns
- Most frequent values for categorical columns
Step 3: Quality Assessment
Check for:
- Missing or null values (empty strings, "NA", "null", "N/A")
- Duplicate rows
- Inconsistent formatting (mixed date formats, case inconsistency)
- Potential outliers (values beyond 3 standard deviations)
Step 4: Generate Report
Create a markdown report with:
- Overview: File name, row count, column count
- Schema table: Column name, type, non-null count, unique count
- Statistics table: Min, max, mean, median, std dev for numeric columns
- Quality issues: List of findings with severity (info/warning/error)
- Key findings: Top 3-5 insights from the data
Output Format
The report should be a well-formatted markdown document suitable for inclusion in project documentation. Use tables for structured data and bullet points for findings.
Error Handling
- If the file is not valid CSV, report the issue and suggest the correct format
- If the file is too large (>100MB), sample the first 10,000 rows and note the sampling
- If encoding errors occur, try UTF-8, Latin-1, and CP1252 in order
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 · 50 lines · 17 tokens per session scan A 054f85edbfec
csv-analysis is a skill published in the GitHub repository codebytes/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 417 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…