What does the Paper Labeler AI skill do?

Extract, filter, and label research papers from rawdata files (.bib, .html) using the two-level taxonomy defined in SKILL.md. Use this skill when the user wants to process new venue data, label papers, add new conferences to the database, run the paper classification pipeline, rebuild the website, or batch-process a folder of rawdata. Triggers: "label papers", "process rawdata", "process folder", "batch process", "add venue", "classify papers", "extract papers from", "run the labeling pipeline", "process .bib", "process .html", "rebuild website", "update website".

Why use Paper Labeler on TypingMind?

Because you install it once and use it with any model. Paper Labeler is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Labeler in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PurCL/ASE/tree/main/.claude/skills/paper-labeler. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Labeler?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Labeler?

As many as you like. As long as a model supports skills, you can use Paper Labeler with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Labeler AI skill free?

It is published on GitHub by PurCL. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Paper Labeler by PurCL — AI Skill for Any LLM

Paper Labeler

Classify LLM-for-code research papers from conference proceedings into a two-level label taxonomy.

Pipeline Overview

Four steps, run in sequence:

extract_papers.py — Parse raw venue files into a uniform JSON format
- For NDSS: also run fetch_ndss_abstracts.py to scrape abstracts from paper pages
label_papers.py — Filter by relevance keywords, then label with Claude API
merge_labeldata.py — Merge results into data/labeldata/labeldata.json
build_site.py — Rebuild the website from updated labeldata

Batch mode:

process_folder.py — Scan a rawdata folder, skip venues already in data/venues.json, and run the full pipeline (extract → label → merge → rebuild) for new venues only.

Additionally:

import_original.py — Import papers from data/rawdata/original.json that are missing from labeldata, mapping old-style labels to the current taxonomy via Claude API. Avoids duplicates.

All scripts are in .claude/skills/paper-labeler/scripts/.

Step-by-Step Workflow

Batch Mode: Process a Folder

Scan a rawdata folder, skip venues already in data/venues.json, and run the full pipeline for each new venue. This is the simplest way to process new data.

bash
# Process all unprocessed venues under data/rawdata/
python .claude/skills/paper-labeler/scripts/process_folder.py

# Process a specific year folder
python .claude/skills/paper-labeler/scripts/process_folder.py data/rawdata/2025/

# Dry-run: show which venues would be processed
python .claude/skills/paper-labeler/scripts/process_folder.py --dry-run

# Filter only (no API calls, no merge)
python .claude/skills/paper-labeler/scripts/process_folder.py --filter-only

Options:

--dry-run — show which venues are new vs already processed, no action
--filter-only — keyword filter only, no Claude API calls or merge
--model MODEL — Bedrock model ID (default: us.anthropic.claude-sonnet-4-6)
--region REGION — AWS region (default: us-east-1)
--delay SECONDS — delay between API calls (default: 0.5)
--no-rebuild — skip website rebuild after processing

The script:

Reads data/venues.json to get already-processed venues
Scans the folder for .bib and .html files, groups them by canonical venue name
Skips venues already in venues.json
For each new venue: extract → (fetch NDSS abstracts if needed) → filter+label → merge
Updates venues.json after each successful venue
Rebuilds the website once at the end

Venue names are derived from filenames: EMNLP-findings2024.html and EMNLP-main2024.html both map to EMNLP2024, so multiple track files for the same venue are processed together.

Step 1: Extract Papers

Determine the input format and run the extractor.

bash
# BibTeX files (SE/PL/Security venues — most common)
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/ASE2025.bib > /tmp/extracted.json

# ACL Anthology HTML files (ACL, EMNLP, NAACL)
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/ACL2025.html > /tmp/extracted.json

# NDSS HTML files (titles only — abstracts need individual page fetching)
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/NDSS2025.html > /tmp/extracted.json

Format auto-detection works by file extension and content. Override with --format bib|acl|ndss.

URL handling:

BibTeX: uses url field if present; otherwise auto-constructs from doi field (https://doi.org/{doi})
ACL Anthology HTML: extracts URLs from <a href=...> in title links (e.g., https://aclanthology.org/2025.acl-long.1/)
NDSS HTML: extracts URLs from paper links
If a paper's key is a DOI (e.g., 10.5555/...), it can be used as a fallback URL via https://doi.org/{key}

For NDSS: the HTML only contains titles and links (no inline abstracts). After extraction, use fetch_ndss_abstracts.py to scrape abstracts from individual paper pages:

bash
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/NDSS2025.html > /tmp/ndss_extracted.json
python .claude/skills/paper-labeler/scripts/fetch_ndss_abstracts.py /tmp/ndss_extracted.json -o /tmp/extracted.json

Options for fetch_ndss_abstracts.py:

--delay SECONDS — delay between HTTP requests (default: 1.0)
--resume — skip papers already in the output file (for resuming after interruption)

Step 2: Filter and Label

Phase 1 — Keyword filter (no API needed):

bash
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase filter -o /tmp/filtered.json

This keeps papers whose title+abstract mention both LLM-related AND code-related terms. It is deliberately conservative (high recall) to avoid missing relevant papers.

Phase 2 — Claude labeling via AWS Bedrock (requires AWS credentials):

bash
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/filtered.json --phase label -o /tmp/labeled.json

Or run both phases at once:

bash
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase all -o /tmp/labeled.json

Options:

--model MODEL — Bedrock model ID (default: us.anthropic.claude-sonnet-4-6)
--region REGION — AWS region (default: us-east-1)
--delay SECONDS — delay between API calls (default: 0.5)
--dry-run — preview what would be labeled, no API calls

Step 3: Merge into labeldata

bash
python .claude/skills/paper-labeler/scripts/merge_labeldata.py /tmp/labeled.json

Options:

--dry-run — show merge stats without writing
--no-overwrite — skip papers already in labeldata.json
--labeldata PATH — write to a different file

Step 4: Rebuild Website

After merging new papers into labeldata.json, rebuild the website:

bash
python .claude/skills/paper-labeler/scripts/build_site.py

This reads data/labeldata/labeldata.json and generates web/index.html — a single-page app with category sidebar (grouped into three super-groups: Agent for SE, Agent Design and Analysis, Evaluation), search, year/venue filters, and expandable abstracts.

Label Taxonomy

The complete two-level taxonomy is hardcoded below. A paper can belong to multiple categories.

1. Code Generation

Program Synthesis — generating code from natural language descriptions, formal specifications, or input-output examples; includes NL2Code, text-to-SQL, text-to-code, sketch-based synthesis, inductive synthesis, competition programming, code infilling
Code Completion — token-level, line-level, or block-level prediction in editing context; next-token prediction, fill-in-the-middle, IDE autocompletion, inline suggestion, code recommendation
Program Repair — automated bug fixing, patch generation, vulnerability remediation, error correction, program healing, fault repair, security patch, hotfix generation
Code Translation — cross-language migration, transpilation, language porting, API migration, framework migration, legacy modernization, parallel code translation
Decompilation — recovering high-level source code from binary, bytecode, IR, or WebAssembly; lifting, binary-to-source, disassembly-to-C, neural decompilation
Refactoring — code transformation, extract method, rename, move method, code cleanup, style improvement, dead code removal, design pattern application, technical debt reduction

2. Static Analysis

Bug Detection — non-security defects, code smells, anti-patterns, defect prediction, error-prone pattern detection, anomaly detection in code, coding rule violation
Program Verification — formal proof generation, loop invariant synthesis, model checking, theorem proving, Hoare logic, SMT-based verification, bounded model checking, safety property verification, correctness proof, program equivalence
Specification Inference — precondition inference, postcondition inference, contract generation, API specification mining, behavioral specification, protocol inference, function summary generation
Pointer Analysis — points-to analysis, alias analysis, heap modeling, shape analysis, pointer dereference safety, null pointer detection
Call Graph Analysis — call graph construction, function call resolution, virtual dispatch resolution, dynamic dispatch analysis, indirect call analysis, call graph pruning, call graph completeness
Type Inference — type prediction, type annotation generation, gradual typing, type migration, TypeScript/Python type inference, generic type instantiation, type error detection
Data-flow Analysis — reaching definitions, def-use chains, live variable analysis, constant propagation, value-flow analysis, information flow analysis, data dependency analysis
Taint Analysis — taint propagation, source-sink analysis, injection vulnerability detection, untrusted input tracking, sanitization checking, taint policy enforcement
Symbolic Execution — path exploration, constraint solving, concolic execution, symbolic path analysis, path feasibility checking, automatic test input generation via symbolic methods
Abstract Interpretation — numerical abstract domains, interval analysis, polyhedral analysis, widening/narrowing, semantic abstraction, fixpoint computation, over-approximation
Code Summarization — function-level summary, module-level summary, code documentation generation, code explanation, natural language description of code, code narration, code captioning
Code Search — semantic code search, natural language code query, code retrieval, code recommendation, API discovery, example-based code search, cross-lingual code search
Clone Detection — code clone detection, near-miss clone, semantic clone, Type-1/2/3/4 clone, code plagiarism, code similarity measurement, code duplication

3. Dynamic Analysis

Test Case Generation — unit test generation, integration test generation, system test generation, regression test generation, parameterized test, property-based test, boundary value test, test input generation
Test Oracle — assertion generation, expected output inference, metamorphic relation, test oracle derivation, differential oracle, invariant-based oracle, specification-based oracle
Fuzzing — coverage-guided fuzzing, grammar-based fuzzing, mutation-based fuzzing, protocol fuzzing, API fuzzing, kernel fuzzing, IoT fuzzing, seed generation, harness generation, directed fuzzing
Debugging — root cause analysis, automated debugging, fault localization, spectrum-based fault localization, delta debugging, statistical debugging, interactive debugging, failure explanation
Mutation Testing — mutant generation, equivalent mutant detection, mutation score, mutation operator, higher-order mutant, mutation-based test assessment
Bug Reproduction — crash reproduction, bug report reproduction, failure-inducing input, crash deduplication, core dump analysis, reproducing field failures
Domain-Specific Testing — DBMS testing, compiler testing, network protocol testing, kernel testing, GUI testing, mobile app testing, web application testing, embedded system testing, smart contract testing
PoC and Exploit Generation — proof-of-concept generation, exploit synthesis, vulnerability triggering, attack payload construction, CVE reproduction, exploit chain construction

4. Model Safety and Security

Adversarial Attack — adversarial examples against code models, evasion attacks, adversarial perturbation, identifier renaming attack, dead code insertion, semantic-preserving perturbation, adversarial robustness evaluation
Backdoor Detection — trojan detection in code models, backdoor trigger identification, poisoned model detection, neural cleanse for code models, training data poisoning defense, supply chain attack on models, model integrity verification
Memorization — training data extraction, membership inference attack, data contamination detection, benchmark contamination, verbatim memorization, privacy leakage, copyright infringement, data provenance
Secure Code Generation — generating vulnerability-free code, security-aware code generation, CWE-free generation, constrained decoding for security, RLHF for safe code, security policy enforcement during generation
Watermarking — code watermark embedding, watermark detection, AI-generated code identification, code authorship attribution, code provenance verification, model fingerprinting, intellectual property protection
Jailbreaking — bypassing model safety alignment, automated red teaming, safety guardrail evasion, harmful code generation, misuse potential evaluation, safety benchmark, attack taxonomy for code LLMs

5. Agent Safety and Security

Prompt Injection — direct prompt injection, indirect prompt injection, context manipulation, instruction override, data exfiltration via prompt, cross-plugin attack, prompt leaking, goal hijacking
Agent Defense — runtime trace analysis, agent sandboxing, guardrail enforcement, output filtering, safety monitoring, anomaly detection in agent behavior, agent alignment, safe tool invocation
Access Control — permission management, least privilege enforcement, capability restriction, resource access policy, file system access control, network access control, API scope limitation, privilege escalation prevention, credential handling

6. Agent Design

Planning — task decomposition, hierarchical planning, multi-step reasoning, goal-directed planning, plan generation, plan refinement, backtracking, subgoal identification, workflow orchestration, chain-of-thought, tree-of-thought, graph-of-thought
Memory Management — context window management, long-term memory, episodic memory, working memory, conversation history compression, retrieval-augmented generation, knowledge base grounding, repo-level context retrieval, codebase indexing
Tool Use — function calling, API invocation, compiler interaction, debugger integration, test execution, shell command execution, code interpreter, web browsing, database query, external tool orchestration, MCP
Multi-Agent — multi-agent collaboration, pair programming, role-based agent teams, agent communication protocol, task delegation, agent negotiation, debate-based verification, ensemble of agents, agent specialization

7. Code Model

Model Training — pre-training objectives (MLM, CLM, denoising), code-specific pre-training, continual pre-training, parameter-efficient fine-tuning (LoRA, adapter, prefix tuning), instruction tuning, RLHF, DPO, data curation, synthetic data generation, curriculum learning, code tokenization
Binary and IR Model — binary code representation learning, LLVM IR modeling, assembly language model, WebAssembly model, bytecode model, cross-architecture binary analysis, binary similarity, binary function embedding

8. Other SE Tasks

Code Review — automated code review, review comment generation, review score prediction, code change assessment, reviewer recommendation, review quality evaluation, pull request analysis
Doc/Comment/Commit Message Generation — docstring generation, inline comment generation, commit message generation, changelog generation, API documentation, README generation, release note generation, Javadoc
Log Analysis — log parsing, log anomaly detection, logging statement generation, log template extraction, log-based failure diagnosis, log level recommendation, structured logging

9. Evaluation

Benchmark — dataset construction, evaluation framework, leaderboard, test suite, code generation benchmark, analysis benchmark, security benchmark, multi-task benchmark, cross-lingual benchmark, execution-based evaluation, human evaluation protocol
Empirical Study — large-scale evaluation, comparative study, user study, developer survey, ablation study, replication study, industrial case study, mixed-methods study, A/B testing of LLM tools
Survey — literature review, systematic literature review, systematic mapping study, meta-analysis, research roadmap, taxonomy study, state-of-the-art review, tutorial overview

Label Format

Each paper's labels field is a flat list containing both top-level and sub-level labels. Example: ["Static Analysis", "Bug Detection", "Evaluation", "Benchmark"]

Relevance Criteria

A paper is relevant if LLMs or AI agents are a central part of the paper's own contribution — either:

Using LLMs/agents to solve software engineering tasks (code generation, analysis, testing, repair, etc.), OR
Studying properties of code LLMs/agents themselves (safety, security, benchmarking, training)

NOT relevant:

Papers that only mention LLMs/agents in background/related work but don't use or study them
Traditional ML (SVM, random forest), traditional DL (CNN, RNN, GNN) applied to SE — unless specifically targeting code LLMs (e.g., adversarial attacks on code models)
General NLP/vision/speech unrelated to code
Neural network verification, ML compilers, quantum computing, pure PL theory
General SE papers that don't use LLMs/agents as a core component

Output Format

The output JSON matches data/labeldata/labeldata.json schema:

json
{
  "Paper Title": {
    "type": "INPROCEEDINGS",
    "key": "...",
    "author": "...",
    "booktitle": "...",
    "title": "Paper Title",
    "year": "2025",
    "abstract": "...",
    "labels": ["Code Generation", "Program Synthesis"],
    "venue": "ASE2025",
    "url": "...",
    ...
  }
}

Common Patterns

Batch-process all new venues in a folder:

bash
# Dry-run first to see what's new
python .claude/skills/paper-labeler/scripts/process_folder.py data/rawdata/2025/ --dry-run

# Process all new venues (skips already-processed ones)
python .claude/skills/paper-labeler/scripts/process_folder.py data/rawdata/2025/

Process a new SE venue (bib file):

bash
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/ASE2025.bib > /tmp/extracted.json
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase all -o /tmp/labeled_ASE2025.json
python .claude/skills/paper-labeler/scripts/merge_labeldata.py /tmp/labeled_ASE2025.json --dry-run
python .claude/skills/paper-labeler/scripts/merge_labeldata.py /tmp/labeled_ASE2025.json
python .claude/skills/paper-labeler/scripts/build_site.py

Process a new NLP venue (ACL Anthology HTML):

bash
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/ACL2025.html > /tmp/extracted.json
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase all -o /tmp/labeled_ACL2025.json
python .claude/skills/paper-labeler/scripts/merge_labeldata.py /tmp/labeled_ACL2025.json
python .claude/skills/paper-labeler/scripts/build_site.py

Process an NDSS venue (HTML — requires abstract fetching):

bash
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/NDSS2025.html > /tmp/ndss_extracted.json
python .claude/skills/paper-labeler/scripts/fetch_ndss_abstracts.py /tmp/ndss_extracted.json -o /tmp/extracted.json
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase all -o /tmp/labeled_NDSS2025.json
python .claude/skills/paper-labeler/scripts/merge_labeldata.py /tmp/labeled_NDSS2025.json
python .claude/skills/paper-labeler/scripts/build_site.py

Preview without API calls:

bash
python .claude/skills/paper-labeler/scripts/extract_papers.py data/rawdata/2025/CCS2025.bib > /tmp/extracted.json
python .claude/skills/paper-labeler/scripts/label_papers.py /tmp/extracted.json --phase all --dry-run

Note: Phase 2 uses AWS Bedrock (boto3). Ensure AWS credentials are configured (~/.aws/credentials, environment variables, or IAM role). Abstract fetching (fetch_ndss_abstracts.py) requires requests (pip install requests).

Publisher	PurCL
Repository	`ASE`
Skill name	`paper-labeler`
Stars	636
Forks	45
Bundled files	8
Links	Skill source Repository

Paper Labeler

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Paper Labeler

Pipeline Overview

Step-by-Step Workflow

Batch Mode: Process a Folder

Step 1: Extract Papers

Step 2: Filter and Label

Step 3: Merge into labeldata

Step 4: Rebuild Website

Label Taxonomy

1. Code Generation

2. Static Analysis

3. Dynamic Analysis

4. Model Safety and Security

5. Agent Safety and Security

6. Agent Design

7. Code Model

8. Other SE Tasks

9. Evaluation

Label Format

Relevance Criteria

Output Format

Common Patterns

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Paper Labeler

Pipeline Overview

Step-by-Step Workflow

Batch Mode: Process a Folder

Step 1: Extract Papers

Step 2: Filter and Label

Step 3: Merge into labeldata

Step 4: Rebuild Website

Label Taxonomy

1. Code Generation

2. Static Analysis

3. Dynamic Analysis

4. Model Safety and Security

5. Agent Safety and Security

6. Agent Design

7. Code Model

8. Other SE Tasks

9. Evaluation

Label Format

Relevance Criteria

Output Format

Common Patterns

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now