News Aggregator Skill
cclank/news-aggregator-skill
Comprehensive news aggregator that fetches, filters, and deeply analyzes real-time content from 44+ sources including Hacker News, Lobsters, Dev.to, GitHub, arXiv, Hugging Face Papers, AIHOT, TLDR…
Implement text summarization using extractive and abstractive approaches.
Install Algo NLP Summarization
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-nlp-summarization .claude/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.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/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .claude/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarizationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-nlp-summarization .agents/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .agents/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-nlp-summarization .cursor/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .cursor/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-nlp-summarization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-nlp-summarization .gemini/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .gemini/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-nlp-summarization .github/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .github/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-nlp-summarization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-nlp-summarization .opencode/skills/algo-nlp-summarization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-nlp-summarization" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-nlp-summarization into .opencode/skills/algo-nlp-summarization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-nlp-summarization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algo-nlp-summarization- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 393 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Implement text summarization using extractive and abstractive approaches.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to condense long documents
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo NLP Summarization is an agent skill from asgard-ai-platform/skills. Implement text summarization using extractive and abstractive approaches. Use this skill when the user needs to condense long documents, build an automatic summarization pipeline, or compare summarization strategies — even if they say 'summarize this document', 'TLDR', or 'key points extraction'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/factual-consistency.md` and `references/graph-based-extraction.md`).
It sits in Writing & Content, covering Summarization and Natural language processing. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to condense long documents
- Build an automatic summarization pipeline
- Compare summarization strategies — even if they say summarize this document
- Key points extraction
Example prompts
- “summarize this document”
- “key points extraction”
- “/algo-nlp-summarization”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. 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 (its code samples are json).
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
Algo NLP Summarization loads about 1.1k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 393 words of instructions outside code blocks.
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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 393 words, ~1,083 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-nlp-summarization/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-nlp-summarization
- description
- Implement text summarization using extractive and abstractive approaches. Use this skill when the user needs to condense long documents, build an automatic summarization pipeline, or compare summarization strategies — even if they say 'summarize this document', 'TLDR', or 'key points extraction'.
- metadata.category
- WP-45 NLP 演算法
- metadata.tags
- nlp, summarization, extractive, abstractive
Text Summarization
Overview
Text summarization condenses documents while preserving key information. Extractive: selects and concatenates important sentences from the original. Abstractive: generates new text that paraphrases the content. Extractive is simpler and more faithful; abstractive is more fluent but may hallucinate.
When to Use
Trigger conditions:
- Condensing long documents, reports, or article collections
- Building automated summary pipelines for content curation
- Comparing extractive vs abstractive approaches for a use case
When NOT to use:
- When full document understanding is needed (summarization loses detail)
- For structured data extraction (use NER or information extraction)
Algorithm
IRON LAW: Abstractive Summarization Can HALLUCINATE
Abstractive models may generate fluent text containing facts NOT in
the source. Always verify key claims in abstractive summaries against
the original document. For high-stakes use cases (legal, medical),
prefer extractive or use abstractive with factual consistency checking.Phase 1: Input Validation
Determine: input length, target summary length (ratio or word count), single-doc vs multi-doc, domain. Gate: Input text available, target length defined.
Phase 2: Core Algorithm
Extractive (TextRank/LexRank):
- Split document into sentences
- Build similarity graph (sentence nodes, cosine similarity edges)
- Run PageRank on sentence graph
- Select top-k sentences by rank, reorder by original position
Abstractive (transformer-based):
- Use pre-trained model (BART, T5, Pegasus)
- Encode input document (handle length limits with chunking if needed)
- Generate summary with beam search
- Post-process: check for repetition, factual consistency
Phase 3: Verification
Evaluate: ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) against reference summaries. Manual check for factual accuracy and coherence. Gate: ROUGE scores reasonable for domain, no hallucinations in spot-check.
Phase 4: Output
Return summary with metadata.
Output Format
{
"summary": "The company reported Q4 revenue of...",
"method": "extractive_textrank",
"metadata": {"input_words": 2000, "summary_words": 200, "compression_ratio": 0.10, "sentences_selected": 5}
}Examples
Show full SKILL.md (168 more words)Show less
Sample I/O
Input: 2000-word news article about quarterly earnings Expected: 200-word summary covering: revenue, profit, guidance, key highlights. Extractive: 5-6 selected sentences. Abstractive: coherent paragraph.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Very short input (< 100 words) | Return as-is or minimal trimming | Already concise |
| Multiple contradicting sections | Summary may miss nuance | Summarization favors dominant theme |
| Technical jargon | Extractive preserves, abstractive may simplify | Domain expertise affects quality |
Gotchas
- ROUGE ≠ quality: ROUGE measures n-gram overlap with references. A high-ROUGE summary can be incoherent, and a low-ROUGE summary can be excellent with different word choices.
- Input length limits: Transformer models have max token limits (512-4096). Long documents need chunking strategies (chunk-then-summarize or hierarchical summarization).
- Repetition: Abstractive models sometimes repeat phrases. Use repetition penalty during generation (no_repeat_ngram_size).
- Position bias: In news text, important information is front-loaded (inverted pyramid). Simple "take first N sentences" is a strong extractive baseline.
- Multi-document summarization: Summarizing multiple related documents requires handling redundancy and contradiction across sources.
References
- For TextRank/LexRank implementation details, see
references/graph-based-extraction.md - For factual consistency checking, see
references/factual-consistency.md
© asgard-ai-platform, 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 algo-nlp-summarization of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/factual-consistency.md
- references/graph-based-extraction.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo NLP Summarization 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo NLP Summarization this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| News Aggregator Skillcclank/news-aggregator-skill | 1.3k | — | ~2.1k | Automated safety check: Pass | None | |
| AI Daily Newsgeekjourneyx/ai-daily-skill | 235 | — | ~2.3k | Automated safety check: Pass | None | |
| Vss Search ArchiveNVIDIA-AI-Blueprints/video-search-and-summarization | 1.9k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| AnalyzeriBigQiang/feedgrab | 614 | — | ~1k | Automated safety check: Pass | MIT | |
| Reportmicrosoft/data-formulator | 18k | — | ~1.5k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algo NLP Summarization
What does Algo NLP Summarization do?
Implement text summarization using extractive and abstractive approaches. Algo NLP Summarization is an agent skill from asgard-ai-platform/skills. Implement text summarization using extractive and abstractive approaches.
When should I use Algo NLP Summarization?
Algo NLP Summarization fits situations like: the user needs to condense long documents; build an automatic summarization pipeline; compare summarization strategies — even if they say summarize this document; key points extraction.
How do I install Algo NLP Summarization in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a claude-code`. Or copy the skill folder (algo-nlp-summarization in asgard-ai-platform/skills) into .claude/skills/algo-nlp-summarization in your project. Claude Code loads it when a task matches its description.
How do I install Algo NLP Summarization in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-summarization -a codex`. Or copy the skill folder (algo-nlp-summarization in asgard-ai-platform/skills) into .agents/skills/algo-nlp-summarization in your project. Codex loads it when a task matches its description.
Can I use Algo NLP Summarization 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 asgard-ai-platform/skills --skill algo-nlp-summarization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-nlp-summarization, .gemini/skills/algo-nlp-summarization, .github/skills/algo-nlp-summarization and .opencode/skills/algo-nlp-summarization in your project.
What does Algo NLP Summarization need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo NLP Summarization is instructions for the agent only.
Does Algo NLP Summarization 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 Algo NLP Summarization 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 Algo NLP Summarization use?
Algo NLP Summarization 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 Algo NLP Summarization use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 5.3k tokens, read only when the agent opens those files.
What are the alternatives to Algo NLP Summarization?
Skills that share tags, products or a category with Algo NLP Summarization: News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars), AI Daily News (geekjourneyx/ai-daily-skill, 235 stars), Vss Search Archive (NVIDIA-AI-Blueprints/video-search-and-summarization, 1.9k stars) and Analyzer (iBigQiang/feedgrab, 614 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo NLP Summarization?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.