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Found 202 Skills
Extract requirements from existing documents including PDFs, Word docs, meeting transcripts, specifications, and web content. Identifies requirement candidates, categorizes them, and outputs in pre-canonical format.
Use this skill when designing coding challenges, structuring system design interviews, building interview rubrics, calibrating evaluation criteria, or creating hiring loops. Triggers on interview question design, coding assessment creation, system design prompt writing, rubric building, interviewer training, candidate evaluation, and any task requiring structured technical assessment.
Generate migration deliverables for bringing relevant Megatron changes into MindSpeed after branch alignment and impact mapping are complete. Use when Codex already has a confirmed MindSpeed-to-Megatron branch pairing and needs to produce a migration report, candidate patch, or guarded workspace edits instead of redoing upstream analysis from scratch.
Find dead code and cleanup candidates such as unused exports, unreachable branches, orphaned files, stale feature flags, dead registrations, and compatibility layers with no live callers. Use when auditing refactors, bundle-size cleanup, architecture simplification, pre-release cleanup, reviewing requests to find unused code or decide what can be deleted, or when deciding whether code can be safely removed or auto-fixed.
Guides Validation by Educational Experience (VEE) for North American actuarial credential paths (SOA, CAS)—how VEE fits preliminary requirements, current topic areas (Economics, Accounting & Finance, Mathematical Statistics; subject to society updates), approved-course criteria, candidate workflow and documentation, SOA vs CAS submission timing relative to ASA/ACAS progress, international/transfer considerations, and common pitfalls. Use for VEE, validation by educational experience, VEE credit, actuarial VEE requirements, SOA VEE, CAS VEE, VEE economics, VEE statistics, VEE accounting and finance, college credit for actuarial exams, submit VEE transcript—not deep exam study (pre-actuarial-foundations, advanced-short-term-actuarial-mathematics, advanced-long-term-actuarial-mathematics), workpapers (actuarial-analyst), signing (associate-actuary, appointed-chief-actuary), official transcript qualification rulings, or generic degree planning.
Finds and ranks expensive Snowflake queries by cost, time, or data scanned. Use when: (1) User asks to find slow, expensive, or problematic queries (2) Task mentions "query history", "top queries", "most expensive", or "slowest queries" (3) Analyzing warehouse costs or identifying optimization candidates (4) Finding queries that scan the most data or have the most spillage Returns ranked list of queries with metrics and optimization recommendations.
Use this when the user explicitly requests to "write/polish NSFC grant abstract", "generate Chinese and English abstracts", or "translate Chinese abstract to English abstract". Output both Chinese and English versions: The English version must be a faithful translation of the Chinese version (no additional information, no omission of key constraints). The default limit for Chinese abstract is ≤400 characters (including punctuation), and ≤4000 characters for English abstract (including punctuation); the final limit shall prevail as specified in `skills/nsfc-abstract/config.yaml:limits`. Also output **title suggestions**: By default, provide 1 recommended title + 5 candidate titles with justifications (the quantity shall follow `config.yaml:title.title_candidates_default`). Output method: Write the results to `NSFC-ABSTRACTS.md` in the **working directory** (the file name shall follow `config.yaml:output.filename`), which includes in order `# Title Suggestions`, Chinese abstract, English abstract, and length self-check. ⚠️ Not applicable in the following cases: - The user only wants to translate a general text unrelated to grant proposals (direct translation is required instead) - The user only wants to write the main body of project justification/research content/research foundation (use the corresponding NSFC series skill instead)
Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。
Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology. Builds multi-layer networks from ChEMBL, OpenTargets, STRING, DrugBank, Reactome, FAERS, and 60+ other ToolUniverse tools. Calculates Network Pharmacology Scores (0-100), identifies repurposing candidates, predicts mechanisms, and analyzes polypharmacology. Use when users ask about drug repurposing via network analysis, multi-target drug effects, compound-target-disease networks, systems pharmacology, or polypharmacology.
Audits and auto-fixes a project's CLAUDE.md against Anthropic best practices. Activates during ship phase — checks conciseness, enforces @import structure for detailed docs, auto-excludes bloat, identifies hook candidates, and auto-fixes structural issues. Flags content questions for developer review.
Inspect, triage, approve, and merge GitHub Renovate pull requests with gh. When no repository is provided, use the default preset repository set, build a candidate execution plan, and execute only after explicit user confirmation. Use when the user asks to check, batch-handle, approve, or merge Renovate PRs.
Design value propositions for candidate customer segments and help the user choose the strongest one. Use when Codex needs to explain jobs, pains, and gains when needed, check niche-positioning prerequisites, ask one question at a time, present multiple value-proposition options, and write user-confirmed outputs into `opc-doc/`.