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 w95/awesome-claude-corporate-skills --skill data-validationgit clone --depth 1 https://github.com/w95/awesome-claude-corporate-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/w95/awesome-claude-corporate-skills/data-validation)<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/data-validation"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/data-validation/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/w95/awesome-claude-corporate-skills/data-validation"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/data-validation.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 | $0.00046 | $0.02160 |
| Opus 5 | $0.00023 | $0.01080 |
| Sonnet 5 | $0.00009 | $0.00432 |
| Haiku 4.5 | $0.00005 | $0.00216 |
Grade A, and why
data-validation 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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-validation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Validation Skill
Pre-delivery QA checklist, common data analysis pitfalls, result sanity checking, and documentation standards for reproducibility.
Pre-Delivery QA Checklist
Run through this checklist before sharing any analysis with stakeholders.
Data Quality Checks
- Source verification: Confirmed which tables/data sources were used. Are they the right ones for this question?
- Freshness: Data is current enough for the analysis. Noted the "as of" date.
- Completeness: No unexpected gaps in time series or missing segments.
- Null handling: Checked null rates in key columns. Nulls are handled appropriately (excluded, imputed, or flagged).
- Deduplication: Confirmed no double-counting from bad joins or duplicate source records.
- Filter verification: All WHERE clauses and filters are correct. No unintended exclusions.
Calculation Checks
- Aggregation logic: GROUP BY includes all non-aggregated columns. Aggregation level matches the analysis grain.
- Denominator correctness: Rate and percentage calculations use the right denominator. Denominators are non-zero.
- Date alignment: Comparisons use the same time period length. Partial periods are excluded or noted.
- Join correctness: JOIN types are appropriate (INNER vs LEFT). Many-to-many joins haven't inflated counts.
- Metric definitions: Metrics match how stakeholders define them. Any deviations are noted.
- Subtotals sum: Parts add up to the whole where expected. If they don't, explain why (e.g., overlap).
Reasonableness Checks
- Magnitude: Numbers are in a plausible range. Revenue isn't negative. Percentages are between 0-100%.
- Trend continuity: No unexplained jumps or drops in time series.
- Cross-reference: Key numbers match other known sources (dashboards, previous reports, finance data).
- Order of magnitude: Total revenue is in the right ballpark. User counts match known figures.
- Edge cases: What happens at the boundaries? Empty segments, zero-activity periods, new entities.
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.
- 9d ago First seen · 234 lines · 46 tokens per session scan A 48886570df5c
data-validation is a skill published in the GitHub repository w95/awesome-claude-corporate-skills (198 stars, last pushed 6mo ago), licensed MIT. It adds 46 tokens to every session and 2,160 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 2026-09-03.
Other skills, from other repositories
debug
Systematic debugging via logs, health checks, hypothesis-driven investigation. Triggers: debug, error, trace root cause, fix bug, reproduce symptom, investigation.
introspect
Agent self-debugging and recovery. Use when stuck in loops, making repeated errors, or quality degrades. Triggers: introspect, self-debug, stuck, loop, why failing.
fix
Applies targeted fix to known bug/lint error, verifies with same command that surfaced it. Triggers: fix, apply fix, fix bug, fix lint, targeted fix.
git-mastery
Advanced Git: rebase, bisect, reflog, cherry-pick, worktrees, LFS. Triggers: rebase, bisect, cherry-pick, reflog, force push, merge conflict, worktree.
lint
Runs linter+typechecker with auto-detected toolchain (ruff/mypy, eslint/tsc, phpstan, golangci-lint, clippy). Triggers: lint, typecheck, static analysis.
performance-profiling
Performance: golden signals, p50/p95/p99, flame graphs, load testing. Triggers: performance, slow, latency, p99, flame graph, bottleneck, memory leak.