skill-fcakyon-phd-skills-dataset-curation · by Install
$ agentstack add skill-fcakyon-phd-skills-dataset-curation ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-fcakyon-phd-skills-dataset-curation) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Dataset Curation Methodology
You are helping a researcher curate, analyze, or expand a dataset with attention to bias, fairness, and quality.
Step 1: Distribution Analysis
Before any curation action, understand the current state:
Per-Class Distribution
- Count instances per class/label/tag
- Compute imbalance ratio (maxcount / mincount)
- Identify severely underrepresented classes (50k): 80/10/10 or 90/5/5
- Medium (5k-50k): 70/15/15 or 80/10/10
- Small (<5k): k-fold cross-validation preferred
Step 4: Quality Assessment
For labeled datasets, assess annotation quality:
- Inter-annotator agreement: Cohen's kappa, Fleiss' kappa, or Krippendorff's alpha
- Label noise estimation: sample and manually verify N labels
- Edge cases: identify ambiguous examples that annotators might disagree on
- Consistency checks: automated rules for label validity
Step 5: Expansion Recommendations
If the dataset needs more data:
- Priority classes: which classes benefit most from more data
- Source suggestions: where to find more data for underrepresented classes
- Collection strategy: active learning, targeted scraping, synthetic augmentation
- Cost estimation: time and resources for each approach
Step 6: Ethical Review Checklist
Before using or publishing any dataset:
- [ ] Content sensitivity: does the data contain sensitive material?
- [ ] Consent: was data collected with appropriate consent?
- [ ] Privacy: are individuals identifiable? Is anonymization needed?
- [ ] Licensing: are data sources used within their license terms?
- [ ] Potential harms: could the dataset be misused?
- [ ] Documentation: is the dataset documented with a datasheet/data card?
Output Format
Produce:
- Distribution report: per-class counts, imbalance ratios, co-occurrence matrix
- Bias findings: identified biases with severity and actionability
- Split recommendation: stratification strategy with validation results
- Expansion plan: prioritized suggestions for addressing gaps
- Ethics checklist: completed checklist with notes per item
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: fcakyon
- Source: fcakyon/phd-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.