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snapshot-data

snapshot-data is a skill for Claude Code from kclemoveki/agentic-skills-eda. It costs 49 tokens per session (1,052 once invoked), scanned A, original, MIT.

A YAML record that identifies a dataset by its file hash, schema fingerprint, dimensions, and column details. YAML is a human-readable text format for structured settings and records.

In plain words
What is it for?
Use it to version CSV, Parquet, JSON, JSONL, and spreadsheet datasets and verify that future work uses the same data.
Why use it?
It lets later analysis runs check whether the input file or its data structure has changed.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 2repo 5mo ago A scan 49 tokens original MIT

Good fit Use it to version CSV, Parquet, JSON, JSONL, and spreadsheet datasets and verify that future work uses the same data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kclemoveki/agentic-skills-eda/snapshot-data
View source ↗ kclemoveki/agentic-skills-eda
Install

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.

Any agent
npx skills add kclemoveki/agentic-skills-eda --skill snapshot-data
Clone the repo
git clone --depth 1 https://github.com/kclemoveki/agentic-skills-eda

Made for: Claude Code.

Wrote 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.

agentmods badge for snapshot-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/snapshot-data/github.svg)](https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/snapshot-data)
Your own site
<a href="https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/snapshot-data"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/snapshot-data/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.

agentmods 80×15 button for snapshot-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/snapshot-data"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/snapshot-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,052 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01052
Opus 5.5 $0.00020 $0.00421
Sonnet 5.5 $0.00010 $0.00210
Haiku 4.5 $0.00005 $0.00105

Measured 2mo ago against content hash d7e329a9584f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

snapshot-data 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 2mo 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.

skills/snapshot-data/SKILL.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Snapshot Data

Produce a manifest YAML for the dataset at $ARGUMENTS so any future re-run of an analysis can verify that the input data is unchanged. The manifest captures both byte-level identity (file hash) and semantic shape (schema fingerprint).

Principle

Two hashes are not redundant — they answer different questions:

  • sha256 of the file detects any byte-level change, including a benign re-export with different encoding.
  • schema fingerprint detects semantic changes. If the file is re-saved with a different encoding the SHA changes but the fingerprint stays — that tells you the data is the same even if the bytes are not.

Step 1 — Locate and validate the dataset

Resolve $ARGUMENTS to an absolute path. If the file does not exist, fail with a clear error message. Support these formats by extension: .csv, .parquet, .json, .jsonl, .xlsx, .xls. If the format is unknown, fail with a clear error message.

Step 2 — Compute the manifest

Run a Python one-shot via Bash (use python3 -c "..." or write a temp script at /tmp/snapshot.py) that computes and prints YAML to stdout. The script must:

  1. Compute sha256 of the file by reading it in chunks (do not load the whole file in memory for large datasets).
  2. Capture os.stat: size_bytes, modified_at (ISO-8601, UTC).
  3. Load the dataset with the appropriate reader (pd.read_csv etc.) and capture:
    • rows, cols
    • columns (list, in original order)
    • dtypes per column (as strings, e.g. "int64", "object")
    • null_counts per column (only columns with at least 1 null)
  4. Compute dtypes_fingerprint: SHA-256 (first 16 hex chars) of the canonical string <rows>|<sorted_columns>|<sorted_dtypes_tuples> where each pair is name:dtype. Sorting is deliberate so column order does not affect the fingerprint — only schema does.
  5. Capture generation metadata: by: snapshot-data, at: <ISO-8601 UTC now>, format: <inferred from extension>.

The manifest structure must follow this exact YAML layout:

Read the full file on GitHub · 89 lines

Changes

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.

  1. 2mo ago First seen · 89 lines · 49 tokens per session scan A d7e329a9584f

Subscribe to this mod's changes

snapshot-data is a skill published in the GitHub repository kclemoveki/agentic-skills-eda (2 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 1,052 once invoked, about $0.0002 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.

Analytics and metrics
Related

snapshot-data compared with skills from other repositories

snapshot-data next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
snapshot-datakclemoveki/agentic-skills-edaSkill25mo agoMITA49Claude Code
imagegenopenai/codexSkill133kyesterdayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code