
ccf-literature-searcher
Find and verify external literature, prior art, datasets, benchmarks, and citation candidates. Use for 文献检索, 相关工作, benchmark搜索, and research opportunity maps. Recurring recent-paper watch belongs to ccf-literature-monitor; existing-citation audits, manuscript assessment, and result schemas have separate owners.
- Installs
- 1.9K
- Last 24h
- +17
- Trending rank
- #6615
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-literature-searcher- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ccf-literature-searcher automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
CCF Literature Searcher
Family File Contract
Before file work, reuse canonical paths and one task/artifact working directory; default to project-root ccfa-workfiles/<purpose>/<artifact-id>/. Update in place, preserve inputs, and use UTF-8 with Chinese-text checks. Apply artifact-contracts.md for naming, ownership, and task-created cleanup; handoffs inherit these paths.
Collaboration Contract
Before specialist execution, read and apply ccf-humanization first, then ccf-common. At every handoff, reuse their applicable active rules or refresh missing/changed ones. Both preflights are required even without prose; detailed editing, experiment, and maintenance modes run only when relevant.
Integrate necessary specialist contributions under one owner; reuse valid prerequisites and verify affected results. Follow the cooperation routes without duplicate reports.
Invocation Controls
CCFA Handoff Mode: PARTIAL (Recommended). Follow metadata.ccf_skill_controls.handoff_question_mode and ../ccf-common/references/handoff-modes.md. Use ../ccf-common/references/routing.md to keep literature search separate from idea optimization, manuscript writing, experiment design, paper review, and rebuttal.
Load ../ccf-common/references/task-modes.md before deciding exploratory, quick, or standard mode. Use exploratory mode for early direction scouting, "看看还有没有机会", "这个方向是不是被做完了", or literature search meant to feed idea optimization rather than a final novelty verdict. Use quick mode for a narrow related-work scan or a small set of candidate citations. Use standard mode for Related Work, Introduction, mature idea novelty grounding, benchmark discovery, experiment design, or substantial multi-deliverable work. A small citation lookup stays quick even if a writer will use it.
If the user asks for recurring watch, latest-paper monitoring, competitor tracking, "recently any similar idea", arXiv/OpenReview feed scans, or lab/project tracking, route to ccf-literature-monitor by the shared handoff mode. Use this skill for deep retrieval, closest-work clustering, related-work structure, benchmark/dataset discovery, and citation candidates.
Treat user ideas, draft text, unpublished results, and private manuscripts as private material. Load ../ccf-common/references/privacy-and-evidence.md before browsing. Search with public keywords, public titles, venue names, method names, public abstracts, or user-approved query text. Do not paste private draft sentences into a search query unless the user explicitly authorizes it.
Source-quality exclusion: do not search, cite, recommend, or include policy-excluded venues, journals, URLs, or PDFs. The shared policy includes MDPI sources in this exclusion set; record exclusions only in internal screening or the search-notes file.
Core Rule
Ground novelty and positioning using high-quality, inspectable sources. Prefer influential conferences, strong journals, official proceedings pages, archival repositories, and public paper pages. Do not invent papers, citations, venues, links, acceptance status, benchmark status, or numerical results. Separate searched evidence from inference. Assess actual overlap: related work can reveal an open question or establish that a candidate's central claim is already covered. Offer meaningful differentiation when supported; do not force a rescue route or abandon the user's broader objective. Follow the user's requested output shape: short list, related-work clusters, opportunity map, BibTeX candidates, benchmark table, search folder, or handoff summary.
Mandatory Search Checklist
In standard mode, complete this checklist before final output. In quick mode, run the relevant subset and return a compact checklist status.
- The user's topic is converted into safe public search queries.
- Shared source-quality exclusions are applied to search domains, candidates, and final outputs.
- Sources prioritize primary or high-confidence venues: official proceedings, arXiv/OpenReview when appropriate, ACL Anthology, CVF, PMLR, ACM, IEEE, USENIX, DBLP, Semantic Scholar, OpenAlex, Crossref, and venue or project pages.
- Candidate papers are deduplicated by title and linked to a stable URL.
- Each included paper has venue/year/source status, paper type, and relevance rationale.
- Score paper quality on insight, completeness, and experimental numeric evidence only when requested or useful for substantial screening, and only to the extent the inspected text supports it. Pure benchmark papers skip the numeric-results score and receive a benchmark-quality note instead.
- Paper type is one of
pure benchmark,pure method,method + benchmark,survey,system/tool,theory/proof, orother. - Every claim about a paper is traceable to the linked source or marked as inferred.
- For idea-stage searches, report what each closest-work cluster already covers and any supported open question. Offer differentiation only when a meaningful distinction can be explained; otherwise state that no worthwhile distinction was found in the inspected evidence. Return this finding to the idea owner without inventing a rescue module or abandoning the user's broader objective.
- Write a literature-search folder only when reusable output is requested or needed for the authorized work. A bounded helper lookup can return verified evidence in context without a separate report.
- When the search feeds idea optimization, an idea-grounding packet separates source-supported observations from inferred gaps and includes mechanism primitives, protocol anchors, cross-source conflict/open-gap relations, and confidence.
- Optional handoff to
ccf-literature-monitor,ccf-paper-writer,ccf-idea-optimizer,ccf-idea-reviewer,ccf-experiment-designer, orccf-paper-reviewerfollows CCFA handoff mode.
Workflow
-
Identify the search purpose: Related Work, Introduction support, novelty check, direction scouting, idea optimization, idea review, experiment design, benchmark/dataset discovery, or reviewer-risk diagnosis.
-
Create public queries from the user's topic. If the topic is too private or underspecified, ask only for non-sensitive keywords or infer broad keywords with lower confidence.
-
Load
references/search-and-scoring.md. Use these search ranges as starting budgets, not quotas. Stop when the requested clusters, closest competitors, and evidence needs are covered and further retrieval adds no material information; broaden when a specific gap remains. Search breadth defaults:- Exploratory: 10-20 screened candidates, 5-10 final papers or clusters, plus opportunity gaps.
- Quick: 6-10 screened candidates, 3-6 final papers.
- Standard: 15-30 screened candidates, 8-15 final papers unless the user requests another size.
These are planning ranges for standalone searches, not quotas. A helper lookup retrieves only the sources needed to resolve its assigned question and returns to the requesting owner. Honor a requested breadth; do not repeat verified searches or collect extra papers merely to reach a default count.
-
Search discovery indexes first, then verify candidates through stable paper pages or official proceedings when possible. Use broad web search only to find primary links; do not rely on snippets for final claims.
-
Filter by influence and fit. Prefer CCF-A/B conferences, top-field conferences, strong journals, widely used benchmarks, or recent high-signal preprints from credible groups. Exclude low-quality, predatory, inaccessible, or policy-excluded sources. For exploratory searches, include one or two "near miss" or negative-signal clusters if they reveal an open gap, failed assumption, outdated benchmark, missing user group, or neglected system constraint.
-
Classify papers as needed for screening. Score inspected papers only when requested or decision-relevant, using these dimensions:
insight: how clear and non-obvious the central idea is.completeness: method/evaluation/proof/dataset/reproducibility coverage.experimental numeric evidence: strength and relevance of reported numerical evidence; markN/A benchmarkfor pure benchmark papers.
-
Write files only when reusable output is requested or needed within the authorized workflow. Honor no-new-files constraints. Reuse an existing canonical search folder for an update; otherwise use
references/report-template.mdwith the default folder name:
ccfa-workfiles/literature/<topic-slug>/
papers.md
papers.csv # only when structured reuse/export is needed
search-notes.md # only when queries/coverage must persist separately
idea-grounding.md # include when the search feeds idea optimization
Reuse an existing dated or custom search folder instead of renaming it. Store the current search date and source versions inside the report; do not create a new folder for an ordinary update. Derive requested Markdown/CSV views from one screened source set rather than writing independent copies. Skip empty or unrequested score columns and generic status sections.
- If the search feeds another module, provide a compact handoff with canonical source paths and only decision-relevant evidence:
- For writing: closest-work groups, novelty gaps, citation cautions.
- For idea optimization: a compact idea-grounding packet with evidence cards, mechanism primitives, protocol anchors, gap/conflict relations, stale/overcrowded directions, timely pivots, and minimum viable research questions. Keep whole abstracts and generic background out of the handoff.
- For idea review: novelty confidence and likely prior-art risks.
- For literature monitoring: watch queries, tracked competitors, and recurring overlap signals.
- For experiment design: datasets, baselines, metrics, benchmark protocols.
- For paper review: missing related work and baseline risks.
Adaptive Output Contracts
Return the requested artifact first. If the user asks for a list of papers, output the list/table directly. If they ask for Related Work material, output clusters and positioning notes. If they ask for a folder, write the folder and summarize it. Use the following defaults for standard or quick search reports.
For standard search, return:
Search purpose:
Queries used:
Source policy:
Folder written:
Top paper table:
Closest-work clusters:
Opportunity map:
Quality-score rationale:
Benchmark/dataset candidates:
Novelty and positioning risks:
Recommended next module:
Checklist status:
For quick search, return:
Quick search scope:
Top candidates:
High-risk missing literature:
Opportunity hint:
Folder written:
Compact checklist status:
Reference Files
Load only what is needed:
references/search-and-scoring.md: Use for source policy, source-quality exclusions, source tiers, paper-type taxonomy, and scoring anchors.references/report-template.md: Use when writing the literature-search folder files.
Retrieval Execution
Batch independent public-safe query clusters when supported, then deduplicate by DOI/arXiv identifier and normalized title before deeper reading. Verify important claims in the actual paper or primary page, not snippets. Track inspected sections and publication/version status. Do not score an unread method or treat inaccessible results as absent. An unavailable source limits that claim; continue with available primary evidence and report the relevant coverage gap.
FAQ
What does the ccf-literature-searcher skill do?
Find and verify external literature, prior art, datasets, benchmarks, and citation candidates. Use for 文献检索, 相关工作, benchmark搜索, and research opportunity maps. Recurring recent-paper watch belongs to ccf-literature-monitor; existing-citation audits, manuscript assessment, and result schemas have separate owners.
How do I install ccf-literature-searcher?
Run npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-literature-searcher in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ccf-literature-searcher/ (personal) or .claude/skills/ccf-literature-searcher/ (project).
Which AI agents can use ccf-literature-searcher?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ccf-literature-searcher safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ccf-literature-searcher?
It is published by mikubaka88 in mikubaka88/ccfa-skills. It has 1,936 recorded installs.
More from mikubaka88/ccfa-skills
ccf-common
mikubaka88/ccfa-skills
Required shared preflight for every CCFA skill after ccf-humanization: apply routing, scope, prerequisites, evidence rules, handoffs, and artifact contracts. Also maintain and audit these shared rules. Research deliverables remain with their specialist owners.
ccf-paper-writer
mikubaka88/ccfa-skills
Draft, revise, polish, compress, or presentation-adapt CCF manuscript prose. Use for 写作, 润色论文, research documents to complete papers, 完整初稿, 实验留空, 改写, 压缩论文, abstracts, sections, and slides. Draft content before fitting pages; preserve evidence and format. Assessment without rewriting belongs to ccf-paper-reviewer; rebuttal belongs to ccf-rebuttal-writer.
ccf-paper-reviewer
mikubaka88/ccfa-skills
Review manuscript claims, evidence, and writing without rewriting. Use for 文章审核, 审稿, 稿件有什么硬伤, 结论站得住吗, 投稿成熟度, writing review, and version comparison. Return a structured scientific or writing review; scores need not be explicitly requested. Concept-only judgment belongs to ccf-idea-reviewer even with a full PDF; requested prose edits belong to ccf-paper-writer.
ccf-humanization
mikubaka88/ccfa-skills
Required first preflight before every CCFA skill, including research, review, retrieval, experiments, visuals, and maintenance. Keep reasoning and communication direct, remove empty defensive framing, and preserve evidence and uncertainty. Also use for 去防御性 and 论文人性化. Apply prose edits only within the authorized task; specialist skills retain ownership.
ccf-idea-optimizer
mikubaka88/ccfa-skills
Develop and optimize rough CCF research ideas into problems, insights, mechanisms, and evidence plans. Use for 优化idea, 具象化idea, 找方向, 方向探索, and rescue routes when development is the requested deliverable. Judging whether an idea is worthwhile, novel, or coherent belongs to ccf-idea-reviewer even without scores; manuscript edits belong to ccf-paper-writer.
ccf-experiment-designer
mikubaka88/ccfa-skills
Design and interpret CCF experiments: datasets, baselines, metrics, ablations, execution specifications, observed feedback, and result-table contents. Use for 设计实验, 消融, benchmark planning, 实验结果分析, and evidence design within requested autonomous research. Preserve real values. Orchestrator coordinates research-loop execution; visual styling and broad retrieval have separate owners.
Popular media & creative skills
hyperframes
heygen-com/hyperframes
Read first to make, edit, animate or render any video with HyperFrames (HTML rendered to video), including launch films, explainers, music videos, charts, maps, vertical clips, title cards, captions and overlays. Holds the render contract, the work loop and what each kind of video needs.
media-use
heygen-com/hyperframes
Get or make any media for a HyperFrames video - music, sound effects, images, icons, logos, voiceover, avatar and generated video, transcripts, cutouts, colour grades and looks - with HeyGen or free local tools, and mix placed audio. Also for footage that looks dark, flat or dull, or should be stylized or hidden.
hyperframes-registry
heygen-com/hyperframes
About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.
hyperframes-creative
heygen-com/hyperframes
Non-animation creative direction for HyperFrames videos. Use for design spec (frame.md / design.md) handling, palettes, typography, narration, beat planning, audio-reactive visuals, composition patterns, and brand / style decisions. For atomic motion patterns and scene blueprints, use hyperframes-animation.
ai-video-generation
101-skills/superpowers
Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, reference-to-video, video editing, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative, seedance, happyhorse
ai-image-generation
101-skills/superpowers
Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image