ReddRadar
Topic: codex
8,457 skills in this topic.
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optimizing-performance
Analyzes and optimizes application performance across frontend, backend, and database layers. Use when diagnosing slowness, improving load times, optimizing queries, reducing bundle size, or when asked about performance issues.
CloudAI-X/claude-workflow-v2 1,305
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parallel-execution
Patterns for parallel subagent execution using Task tool with run_in_background. Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.
CloudAI-X/claude-workflow-v2 1,305
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security-patterns
Implements authentication, authorization, encryption, secrets management, and security hardening patterns. Use when designing auth flows, managing secrets, configuring CORS, implementing rate limiting, or when asked about JWT, OAuth, password hashing, API keys, RBAC, or security best practices.
CloudAI-X/claude-workflow-v2 1,305
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vercel-react-best-practices
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
CloudAI-X/claude-workflow-v2 1,305
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web-design-guidelines
Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", "check my site against best practices", or "web interface guidelines".
CloudAI-X/claude-workflow-v2 1,305
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_template_reference_first
Internal template for creating or refactoring a skill into the repository's reference-first shape.
**Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill.
**Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.
**Skip if**: the task is a one-off workflow that will not be reused as a skill.
**Network**: none.
**Guardrail**: keep domain knowledge and writing exemplars out of `run.py`; make reference loading explicit; do not ship reader-facing placeholder text.
WILLOSCAR/research-units-pipeline-skills 377
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agent-survey-corpus
Download a small corpus of open-access arXiv survey/review PDFs about LLM agents and extract text for style learning.
**Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey.
**Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons.
**Skip if**: you cannot download PDFs (no network) or you don't want local PDF files.
**Network**: required.
**Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.
WILLOSCAR/research-units-pipeline-skills 377
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anchor-sheet
Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.
**Trigger**: anchor sheet, anchor facts, numeric anchors, evidence hooks, 写作锚点, 数字锚点, 证据钩子.
**Use when**: `outline/evidence_drafts.jsonl` exists and you want stronger, evidence-anchored writing in `sections/*.md`.
**Skip if**: evidence packs are incomplete (fix `evidence-draft` first).
**Network**: none.
**Guardrail**: NO PROSE; do not invent facts; only select from existing evidence snippets/highlights.
WILLOSCAR/research-units-pipeline-skills 377
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appendix-table-writer
Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.
**Trigger**: appendix tables, publishable tables, survey tables, reader tables, 附录表格, 可发表表格, 综述表格.
**Use when**: you have C4 artifacts (evidence packs + anchor sheet + citations) and want tables that look like a real survey (not internal logs).
**Skip if**: `outline/tables_appendix.md` already exists and is refined (>=2 tables; citation-backed; no placeholders; not index-y).
**Network**: none.
**Guardrail**: no invented facts; no pipeline jargon; no paragraph cells; use only keys present in `citations/ref.bib`.
WILLOSCAR/research-units-pipeline-skills 377
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argument-selfloop
Argument self-loop: maintain an argument ledger + premise consistency report for drafted sections.
**Trigger**: argument self-loop, argument chain, premise consistency, section self-check, paragraph contract, 论证自循环, 论证链路, 前提一致性, 段落论证动作.
**Use when**: you are in C5 (PROSE allowed), `sections/*.md` exist, and you want to prevent “smooth but hollow” writing by enforcing argument moves + premise hygiene before merge.
**Skip if**: you are pre-C2 (NO PROSE), or evidence packs are scaffolded/thin (route upstream to `evidence-selfloop` first).
**Network**: none.
**Guardrail**: do not invent facts; do not add/remove/move citation keys; do not move citations across subsections; the argument ledger is an intermediate artifact and must never be inserted into the paper.
WILLOSCAR/research-units-pipeline-skills 377
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artifact-contract-auditor
Audit the workspace against the pipeline artifact contract (DONE outputs + pipeline target_artifacts).
Writes `output/CONTRACT_REPORT.md`.
**Trigger**: contract audit, artifact contract, missing artifacts, target_artifacts, CONTRACT_REPORT.
**Use when**: you want an auditable PASS/FAIL view of whether a workspace is complete and self-contained (end of run or before sharing).
**Skip if**: you are still intentionally mid-run and don’t care about completeness yet (but it’s still useful as a snapshot).
**Network**: none.
**Guardrail**: analysis-only; do not edit content artifacts; only write the report.
WILLOSCAR/research-units-pipeline-skills 377
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arxiv-search
Retrieve paper metadata from arXiv using keyword queries and save results as JSONL (`papers/papers_raw.jsonl`).
**Trigger**: arXiv, arxiv, paper search, metadata retrieval, 文献检索, 论文检索, 拉取元数据, 离线导入.
**Use when**: 需要一个初始论文集合（survey/snapshot 的 Stage C1），来源为 arXiv（在线检索或离线导入 export）。
**Skip if**: 已经有可用的 `papers/papers_raw.jsonl`，或数据源不是 arXiv。
**Network**: 在线检索需要网络；离线 `--input <export.*>` 不需要网络。
**Guardrail**: 只做 metadata；不要在 `output/` 写长 prose。
WILLOSCAR/research-units-pipeline-skills 377
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bias-assessor
Add bias/risk-of-bias assessment fields to an extraction table and populate them consistently.
**Trigger**: bias, risk-of-bias, RoB, evidence quality, 偏倚评估, 证据质量.
**Use when**: systematic review 已生成 `papers/extraction_table.csv`，需要在 synthesis 前补齐偏倚/质量字段。
**Skip if**: 不是 systematic review，或还没有 `papers/extraction_table.csv`。
**Network**: none.
**Guardrail**: 使用简单可复核刻度（low/unclear/high）+ 简短 notes；保持字段一致性。
WILLOSCAR/research-units-pipeline-skills 377
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chapter-briefs
Build per-chapter (H2) writing briefs (NO PROSE) so the final survey reads like a paper (chapter leads + cross-H3 coherence) without inflating the ToC.
**Trigger**: chapter briefs, H2 briefs, chapter lead plan, section intent, 章节意图, 章节导读, H2 卡片.
**Use when**: `outline/outline.yml` + `outline/subsection_briefs.jsonl` exist and you want thicker chapters (fewer headings, more logic).
**Skip if**: the outline is still changing heavily (fix outline/mapping first).
**Network**: none.
**Guardrail**: NO PROSE; do not invent papers; only reference subsection ids and already-mapped papers.
WILLOSCAR/research-units-pipeline-skills 377
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chapter-lead-writer
Write H2 chapter lead blocks (`sections/S<sec_id>_lead.md`) that preview the chapter's comparison lens and connect its H3 subsections, without adding new facts.
**Trigger**: chapter lead writer, section lead writer, H2 lead, lead paragraph, 章节导读, 章节导语.
**Use when**: you have H2 chapters with multiple H3 subsections and the draft reads like paragraph islands across subsections.
**Skip if**: the outline has no H3 subsections, or `outline/chapter_briefs.jsonl` is missing.
**Network**: none.
**Guardrail**: no new facts/citations; no headings; no narration templates; use only citation keys present in `citations/ref.bib`.
WILLOSCAR/research-units-pipeline-skills 377
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chapter-skeleton
Build a retrieval-informed chapter skeleton (`outline/chapter_skeleton.yml`) from taxonomy/core scope before stable H3 decomposition.
**Trigger**: chapter skeleton, chapter-level outline, H2 skeleton, section-first survey, 章节骨架, 章级骨架.
**Use when**: survey structure should stabilize chapter-level intent before subsection mapping and writing cards.
**Skip if**: `outline/chapter_skeleton.yml` already exists and is refined.
**Network**: none.
**Guardrail**: NO PROSE; do not invent papers; keep output chapter-level only.
WILLOSCAR/research-units-pipeline-skills 377
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citation-anchoring
Regression-check citation anchoring (citations stay in the same subsection) to prevent “polish drift” that breaks claim→evidence alignment.
**Trigger**: citation anchoring, citation drift, regression, cite stability, 引用锚定, 引用漂移.
**Use when**: after editing/polishing, you want to confirm citations did not migrate across `###` subsections.
**Skip if**: you do not have a baseline anchor file yet.
**Network**: none.
**Guardrail**: analysis-only; do not edit content.
WILLOSCAR/research-units-pipeline-skills 377
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citation-diversifier
Raise citation diversity/density (NO NEW FACTS): generate an in-scope “citation budget” plan per H3 so drafts stop failing the global unique-citation gate and stop looking under-cited.
**Trigger**: cite boost, citation budget, unique citations too low, add more citations, improve reference density, 引用太少, 增加引用, 引用密度.
**Use when**: `pipeline-auditor` FAILs due to low unique citations, or you want to increase cite density without changing claims.
**Skip if**: you need new papers (fix C1/C2 mapping first), or `citations/ref.bib` / `outline/writer_context_packs.jsonl` is missing.
**Network**: none.
**Guardrail**: NO NEW FACTS; do not invent citations; only use keys already present in `citations/ref.bib`; keep citations within each H3’s allowed scope (`outline/writer_context_packs.jsonl` / `outline/evidence_bindings.jsonl`).
WILLOSCAR/research-units-pipeline-skills 377
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citation-injector
Apply a `citation-diversifier` budget report by injecting *in-scope* citations into an existing draft (NO NEW FACTS), so the run passes the global unique-citation gate without citation dumps.
**Trigger**: citation injector, apply citation budget, inject citations, add citations safely, 引用注入, 按预算加引用, 引用增密.
**Use when**: `output/CITATION_BUDGET_REPORT.md` exists and you need to raise *global* unique citations (or reduce over-reuse) before `draft-polisher` / `pipeline-auditor`.
**Skip if**: you need more papers/citations upstream (fix C1/C2 mapping first), or `citations/ref.bib` is missing.
**Network**: none.
**Guardrail**: NO NEW FACTS; do not invent citations; only inject keys present in `citations/ref.bib`; keep injected citations within each H3’s allowed scope (via the budget report); avoid citation-dump paragraphs (embed cites per work).
WILLOSCAR/research-units-pipeline-skills 377
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citation-verifier
Generate and verify BibTeX entries from paper notes, writing `citations/ref.bib` and `citations/verified.jsonl`.
**Trigger**: citation, BibTeX, ref.bib, verified.jsonl, references, 引用, 参考文献.
**Use when**: 已有 `papers/paper_notes.jsonl`，需要为 prose/LaTeX 准备可追溯的引用（每条都有 url/date/title 验证记录）。
**Skip if**: 还没有 paper notes（或本次产出不需要引用/参考文献）。
**Network**: 自动验证通常需要网络；无网络时可先 record，再标注 needs manual verification。
**Guardrail**: 每个 BibTeX entry 必须对应一条 `citations/verified.jsonl` 记录；prose 只能使用已存在于 `citations/ref.bib` 的 citation keys。
WILLOSCAR/research-units-pipeline-skills 377
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claim-evidence-matrix
Build a section-by-section claim–evidence matrix (`outline/claim_evidence_matrix.md`) from the outline and paper notes.
**Trigger**: claim–evidence matrix, evidence mapping, 证据矩阵, 主张-证据对齐.
**Use when**: 写 prose 之前需要把每个小节的可检验主张与证据来源显式化（outline + paper notes 已就绪）。
**Skip if**: 缺少 `outline/outline.yml` 或 `papers/paper_notes.jsonl`。
**Network**: none.
**Guardrail**: bullets-only（NO PROSE）；每个 claim 至少 2 个证据来源（或显式说明例外）。
WILLOSCAR/research-units-pipeline-skills 377
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claim-matrix-rewriter
Rewrite `outline/claim_evidence_matrix.md` as a projection/index of evidence packs (NO PROSE), so claims/axes are driven by `outline/evidence_drafts.jsonl` rather than outline placeholders.
**Trigger**: claim matrix rewriter, rewrite claim-evidence matrix, evidence-first claim matrix, matrix index, 证据矩阵重写, 从证据包生成矩阵.
**Use when**: `outline/subsection_briefs.jsonl` + `outline/evidence_drafts.jsonl` are ready and you want a clean claim→evidence index for QA/writing.
**Skip if**: `outline/claim_evidence_matrix.md` is already refined and consistent with evidence packs.
**Network**: none.
**Guardrail**: NO PROSE; do not invent facts; only cite keys present in `citations/ref.bib`; if evidence is abstract/title-only, claims must be provisional.
WILLOSCAR/research-units-pipeline-skills 377
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claims-extractor
Extract key claims, contributions, and assumptions from a paper/manuscript into `output/CLAIMS.md` with traceability to source locations.
**Trigger**: claims extractor, extract claims, contributions, assumptions, peer review, 审稿, 主张提取.
**Use when**: 审稿/评审或 evidence audit，需要把主张列表落盘并可追溯到原文位置（section/page/quote）。
**Skip if**: 没有可用的稿件/全文（例如缺少 `output/PAPER.md` 或等价文本）。
**Network**: none.
**Guardrail**: 每条 claim 必须带可定位的 source pointer；区分 empirical vs conceptual claims。
WILLOSCAR/research-units-pipeline-skills 377
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concept-graph
Build a concept graph (nodes + prerequisite edges) from a tutorial spec, saving as `outline/concept_graph.yml`.
**Trigger**: concept graph, prerequisite graph, dependency graph, 概念图, 先修关系.
**Use when**: tutorial pipeline 的结构阶段（C2），需要把教程知识点拆成可排序的依赖图（在写教程 prose 前）。
**Skip if**: 还没有 tutorial spec（例如缺少 `output/TUTORIAL_SPEC.md`）。
**Network**: none.
**Guardrail**: 只做结构；避免写长 prose 段落。
WILLOSCAR/research-units-pipeline-skills 377