New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Ccf Literature Searcher

by mikubaka88 in mikubaka88/CCFA-Skills

Find and verify external literature, prior art, datasets, benchmarks, and citation candidates.

MITAuto-check passedResearch & Science

Install Ccf Literature Searcher

skills CLI
$ npx skills add mikubaka88/CCFA-Skills --skill ccf-literature-searcher -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mikubaka88/CCFA-Skills ccf-literature-searcher --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mikubaka88/CCFA-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ccf-literature-searcher .claude/skills/ccf-literature-searcher && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ccf-literature-searcher
GitHub stars
3k
Token cost
~3.1k tokens
SKILL.md length
1,444 words
Files
4 (incl. references)
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Find and verify external literature, prior art, datasets, benchmarks, and citation candidates.

  • Works in 12 steps: The user's topic is converted into safe… → Shared source-quality exclusions are… → Sources prioritize primary or… → …
  • Research opportunity maps
  • SKILL.md covers Family File Contract, Collaboration Contract, Invocation Controls and Core Rule, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ccf Literature Searcher is an agent skill from mikubaka88/CCFA-Skills. 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.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/report-template.md` and `references/search-and-scoring.md`).

It sits in Research & Science, covering Intellectual property and Citation management. The repository describes itself as: A skill family for shaping the research storyline of CCF-A papers. The licence is MIT.

When your agent uses it

  • Research opportunity maps
  • Tasks that involve Intellectual property
  • Tasks that involve Citation management

Example prompts

  • “/ccf-literature-searcher”

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. The user's topic is converted into safe public search queries.
  2. Shared source-quality exclusions are applied to search domains, candidates, and final outputs.
  3. Sources prioritize primary or high-confidence venues: official proceedings, arXiv/OpenReview when appropriate, ACL Anthology, CVF, PMLR…
  4. Candidate papers are deduplicated by title and linked to a stable URL.
  5. Each included paper has venue/year/source status, paper type, and relevance rationale.
  6. Score paper quality on insight, completeness, and experimental numeric evidence only when requested or useful for substantial screening…
  7. Paper type is one of pure benchmark, pure method, method + benchmark, survey, system/tool, theory/proof, or other.
  8. Every claim about a paper is traceable to the linked source or marked as inferred.
  9. For idea-stage searches, each closest-work cluster includes what is already covered, what remains under-tested, and at least one possible…
  10. Write a literature-search folder only when reusable output is requested or needed for the authorized work. A bounded helper lookup can…
  11. When the search feeds idea optimization, an idea-grounding packet separates source-supported observations from inferred gaps and includes…
  12. Optional handoff to ccf-literature-monitor, ccf-paper-writer, ccf-idea-optimizer, ccf-idea-reviewer, ccf-experiment-designer, or…

What it can do on your machine

Read from SKILL.md and the folder at commit 5969e6b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ccf Literature Searcher loads about 3.1k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 1,444 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mikubaka88/CCFA-Skills at commit 5969e6b, republished under its MIT licence (© mikubaka88). 1,444 words, ~3,114 tokens.

Download SKILL.mdSave it as .claude/skills/ccf-literature-searcher/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
ccf-literature-searcher
description
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.

CCF Literature Searcher

Family File Contract

Before writing, resolve the canonical output and one stable working directory per task/artifact. Reuse explicit or established task paths; otherwise use project-root ccfa-workfiles/<purpose>/<artifact-id>/, with source/, assets/, cache/, and build/ only as needed. Update current files in place; do not scatter intermediates or create iteration copies. Preserve inputs and required evidence; clean only verified disposable files created by this task. Use UTF-8 text I/O and check Chinese text after saving or rendering. For file work, apply artifact-contracts.md and reuse the same paths across skill transitions.

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.

Keep one integrating owner and actively use other skills to resolve missing prerequisites or check material findings. Reuse applicable evidence; do not skip necessary groundwork to save tokens. Before finalizing, integrate contributions and verify affected results. Follow the conditional cooperation routes; avoid unrelated stages and 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. Literature search is not a kill gate: the presence of related work should produce differentiation options, open gaps, benchmark/evidence choices, and caution labels before any "direction is covered" conclusion. 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.

  1. The user's topic is converted into safe public search queries.
  2. Shared source-quality exclusions are applied to search domains, candidates, and final outputs.
  3. 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.
  4. Candidate papers are deduplicated by title and linked to a stable URL.
  5. Each included paper has venue/year/source status, paper type, and relevance rationale.
  6. 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.
  7. Paper type is one of pure benchmark, pure method, method + benchmark, survey, system/tool, theory/proof, or other.
  8. Every claim about a paper is traceable to the linked source or marked as inferred.
  9. For idea-stage searches, each closest-work cluster includes what is already covered, what remains under-tested, and at least one possible differentiation or rescue route.
  10. 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.
  11. 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.
  12. Optional handoff to ccf-literature-monitor, ccf-paper-writer, ccf-idea-optimizer, ccf-idea-reviewer, ccf-experiment-designer, or ccf-paper-reviewer follows CCFA handoff mode.
Show full SKILL.md (662 more words)Show less

Workflow

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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; mark N/A benchmark for pure benchmark papers.

  7. 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.md with the default folder name:

text
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.

  1. 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:

text
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:

text
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.

© mikubaka88, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in ccf-literature-searcher of mikubaka88/CCFA-Skills.

  • SKILL.md
  • agents/openai.yaml
  • references/report-template.md
  • references/search-and-scoring.md

Open the folder on GitHubat commit 5969e6b

Compare with similar skills

Ccf Literature Searcher next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ccf Literature Searcher compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ccf Literature Searcher this skillmikubaka88/CCFA-Skills3k—~3.1kAutomated safety check: PassMIT
Aminer MCP ResearchDrchronx/ai-agent-research-starter-kit139—~1.1kAutomated safety check: PassCustom licence
NeuroarxivUditAkhourii/neuroarxiv433—~3.1kAutomated safety check: PassMIT
Deltasci Groundboheling/deltasci144—~2kAutomated safety check: PassMIT
Patsnap Scientific Literature Journalspatsnap/mcp113—~673Automated safety check: PassApache-2.0
Imc Related Workbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.1kAutomated safety check: PassMIT

Similar skills

  • Aminer MCP Research

    Drchronx/ai-agent-research-starter-kit

    Use AMiner MCP for academic knowledge graph tasks: scholar search, author profile lookup, institution or team analysis, paper and patent discovery, academic influence checks, and research trend…

    139 GitHub stars~1.1k tokensUpdated 4 mo ago
    Research & ScienceAuto-check passed

  • Neuroarxiv

    UditAkhourii/neuroarxiv

    Grounds a coding agent's architecture decisions in real arXiv prior art before it builds something new.

    433 GitHub stars~3.1k tokensUpdated 24 days ago
    Research & ScienceAuto-check passed

  • Deltasci Ground

    boheling/deltasci

    The grounding layer for AI-assisted research: scan the real prior art around an idea, gauge how crowded or open the gap is, and verify every citation against the source of truth.

    144 GitHub stars~2k tokensUpdated 4 mo ago
    Research & ScienceAuto-check passed

  • Patsnap Scientific Literature Journals

    patsnap/mcp

    Patsnap Scientific Literature & Journals MCP for AI agents. An agent skill from patsnap/mcp.

    113 GitHub stars~673 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

  • Imc Related Work

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when writing or auditing the related-work and positioning of an ACM IMC paper, covering the measurement literature lanes, positioning against prior datasets and vantage…

    1.2k GitHub stars~1.1k tokensUpdated 14 days ago
    Research & ScienceAuto-check passed

  • Ijoc Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when staking the computational/methodological contribution of an INFORMS Journal on Computing (IJOC) manuscript against OR/MS and CS prior art.

    1.2k GitHub stars~1.4k tokensUpdated 14 days ago
    Marketing & SEOAuto-check passed

More from mikubaka88/CCFA-Skills

All 17 skills in this repo

  • Ccf Paper Reviewer

    mikubaka88/CCFA-Skills

    Review manuscript claims, evidence, and writing without rewriting.

    3k GitHub stars~3k tokensUpdated 25 days ago
    Auto-check passed

  • Ccf Rebuttal Writer

    mikubaka88/CCFA-Skills

    Write CCF rebuttals, author responses, revision ledgers, response letters, and conservative resubmission plans.

    3k GitHub stars~1.6k tokensUpdated 25 days ago
    Auto-check passed

  • 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.

    3k GitHub stars~1.8k tokensUpdated 25 days ago
    Auto-check passed

  • Ccf Experiment Designer

    mikubaka88/CCFA-Skills

    Design CCF experiment protocols and evidence schemas: datasets, baselines, metrics, ablations, and result-table contents.

    3k GitHub stars~1.9k tokensUpdated 25 days ago
    Auto-check passed

  • Ccf Humanization

    mikubaka88/CCFA-Skills

    Required first preflight before every CCFA skill, including research, review, retrieval, experiments, visuals, and maintenance.

    3k GitHub stars~2k tokensUpdated 25 days ago
    Auto-check passed

  • Ccf Literature Monitor

    mikubaka88/CCFA-Skills

    Scan recent arXiv/OpenReview papers, venue feeds, labs, and competitors for overlap.

    3k GitHub stars~1.8k tokensUpdated 25 days ago
    Auto-check passed

Categories

Questions about Ccf Literature Searcher

What does Ccf Literature Searcher do?

Find and verify external literature, prior art, datasets, benchmarks, and citation candidates. Ccf Literature Searcher is an agent skill from mikubaka88/CCFA-Skills. Find and verify external literature, prior art, datasets, benchmarks, and citation candidates.

When should I use Ccf Literature Searcher?

Ccf Literature Searcher fits situations like: research opportunity maps; tasks that involve Intellectual property; tasks that involve Citation management.

How do I install Ccf Literature Searcher in Claude Code?

Run `npx skills add mikubaka88/CCFA-Skills --skill ccf-literature-searcher -a claude-code`. Or copy the skill folder (ccf-literature-searcher in mikubaka88/CCFA-Skills) into .claude/skills/ccf-literature-searcher in your project. Claude Code loads it when a task matches its description.

How do I install Ccf Literature Searcher in Codex?

Run `npx skills add mikubaka88/CCFA-Skills --skill ccf-literature-searcher -a codex`. Or copy the skill folder (ccf-literature-searcher in mikubaka88/CCFA-Skills) into .agents/skills/ccf-literature-searcher in your project. Codex loads it when a task matches its description.

Can I use Ccf Literature Searcher in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mikubaka88/CCFA-Skills --skill ccf-literature-searcher -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ccf-literature-searcher, .gemini/skills/ccf-literature-searcher, .github/skills/ccf-literature-searcher and .opencode/skills/ccf-literature-searcher in your project.

What does Ccf Literature Searcher need to run?

SKILL.md names no scripts, command-line tools or credentials: Ccf Literature Searcher is instructions for the agent only.

Does Ccf Literature Searcher access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ccf Literature Searcher safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ccf Literature Searcher use?

Ccf Literature Searcher is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ccf Literature Searcher use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.

What are the alternatives to Ccf Literature Searcher?

Skills that share tags, products or a category with Ccf Literature Searcher: Aminer MCP Research (Drchronx/ai-agent-research-starter-kit, 139 stars), Neuroarxiv (UditAkhourii/neuroarxiv, 433 stars), Deltasci Ground (boheling/deltasci, 144 stars) and Patsnap Scientific Literature Journals (patsnap/mcp, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ccf Literature Searcher?

mikubaka88 (a GitHub user) maintains it in mikubaka88/CCFA-Skills, which has 3,015 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on September 16, 2026.

Source: mikubaka88/CCFA-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.