Firecrawl Search Integration
firecrawl/firecrawl
Guidance for adding Firecrawl's /search endpoint to product code and agent workflows when a feature starts from a query rather than a URL.
Implement TF-IDF scoring to measure term importance relative to a document corpus.
Install Algo SEO Tfidf
$ npx skills add asgard-ai-platform/skills --skill algo-seo-tfidf -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-tfidf --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-seo-tfidf .claude/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .claude/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidfType 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-seo-tfidf -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-tfidf --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-seo-tfidf .agents/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .agents/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidf -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-tfidf --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-seo-tfidf .cursor/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .cursor/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidf--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-seo-tfidf -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-tfidf --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-seo-tfidf .gemini/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .gemini/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidfInstalls 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-seo-tfidf -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-seo-tfidf .github/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .github/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidf -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-seo-tfidf --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-seo-tfidf .opencode/skills/algo-seo-tfidf && 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-seo-tfidf" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-tfidf into .opencode/skills/algo-seo-tfidf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-tfidf", 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-seo-tfidf- GitHub stars
- 242
- Token cost
- ~953 tokens
- SKILL.md length
- 374 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Implement TF-IDF scoring to measure term importance relative to a document corpus.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to rank documents by keyword relevance
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 4 more sections
- Runs Python scripts from its folder; calls python
What it does
Algo SEO Tfidf is an agent skill from asgard-ai-platform/skills. Implement TF-IDF scoring to measure term importance relative to a document corpus. Use this skill when the user needs to rank documents by keyword relevance, extract important terms from text, or build a basic search relevance engine — even if they say 'find relevant documents', 'keyword extraction', or 'term importance'.
Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `examples/sample_input.json`, `references/bm25-comparison.md` and `references/inverted-index.md`).
It sits in Backend & APIs, covering Search implementation. 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 rank documents by keyword relevance
- Extract important terms from text
- Build a basic search relevance engine — even if they say find relevant documents
- Keyword extraction
Example prompts
- “find relevant documents”
- “keyword extraction”
- “term importance”
- “/algo-seo-tfidf”
Requirements
- Python 3
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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
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 SEO Tfidf loads about 953 tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 85 tokens; SKILL.md has 374 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); the scripts in this folder are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 374 words, ~953 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-seo-tfidf/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- algo-seo-tfidf
- description
- Implement TF-IDF scoring to measure term importance relative to a document corpus. Use this skill when the user needs to rank documents by keyword relevance, extract important terms from text, or build a basic search relevance engine — even if they say 'find relevant documents', 'keyword extraction', or 'term importance'.
- metadata.category
- WP-35 SEO 演算法
- metadata.tags
- seo, tfidf, information-retrieval, text-analysis
TF-IDF
Overview
TF-IDF (Term Frequency–Inverse Document Frequency) scores term importance as TF(t,d) × IDF(t). High scores mean a term is frequent in a document but rare across the corpus. Computes in O(N × V) where N is documents and V is vocabulary size.
When to Use
Trigger conditions:
- Ranking documents by keyword relevance
- Extracting distinguishing terms from documents
- Building lightweight search without ML models
When NOT to use:
- When semantic similarity matters (use embeddings instead)
- When you need ranking with link authority (combine with PageRank)
Algorithm
IRON LAW: TF-IDF Measures RELATIVE Importance
- A term with high TF but low IDF is common, NOT important
- TF-IDF = TF(t,d) × log(N / DF(t))
- A term appearing in ALL documents has IDF = 0 → score = 0Phase 1: Input Validation
Tokenize documents, apply lowercasing, remove stop words. Build vocabulary. Gate: All documents tokenized, vocabulary size reasonable.
Phase 2: Core Algorithm
- Compute TF(t,d) for each term in each document (raw count, log-normalized, or boolean)
- Compute IDF(t) = log(N / DF(t)) where DF(t) = number of documents containing term t
- Compute TF-IDF(t,d) = TF(t,d) × IDF(t)
- Optionally L2-normalize document vectors for cosine similarity
Phase 3: Verification
Check: terms appearing in all documents have IDF ≈ 0. Rare terms have high IDF. Gate: Score distribution is reasonable; common words score low.
Phase 4: Output
Return scored terms per document or ranked documents per query.
Output Format
{
"query_results": [{"document": "doc_id", "score": 0.73, "matching_terms": ["term1", "term2"]}],
"metadata": {"corpus_size": 1000, "vocabulary_size": 5000, "tf_variant": "log_normalized"}
}Examples
Sample I/O
Input: Corpus: ["the cat sat", "the dog sat", "the cat played"], Query: "cat" Expected: TF("cat", doc1)=1/3, DF("cat")=2, IDF=log(3/2)=0.405. TF-IDF(doc1)=0.135, TF-IDF(doc3)=0.135, TF-IDF(doc2)=0
Show full SKILL.md (161 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Term in all docs | Score = 0 | IDF = log(N/N) = 0 |
| Term in one doc | Highest IDF | log(N/1) = log(N) |
| Empty document | All scores = 0 | No terms to score |
Gotchas
- Stop words matter: Without stop word removal, "the", "is", "a" dominate TF but have zero IDF. Preprocess properly.
- TF variant choice: Raw count, log(1+count), or boolean TF produce very different rankings. Log normalization prevents long documents from dominating.
- IDF smoothing: Add 1 to denominator to avoid division by zero for unknown query terms: IDF = log(N / (DF+1)) + 1.
- Not semantic: "car" and "automobile" are treated as completely different terms. TF-IDF has no concept of synonymy.
- Corpus dependency: IDF values change when the corpus changes. Adding documents alters all scores.
Scripts
| Script | Description | Usage |
|---|---|---|
scripts/tfidf.py | Compute TF-IDF vectors, top terms per document, and query scoring | python scripts/tfidf.py --help |
Run python scripts/tfidf.py --verify to execute built-in sanity tests.
References
- For BM25 (improved TF-IDF), see
references/bm25-comparison.md - For efficient inverted index implementation, see
references/inverted-index.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 4 other files (scripts, references) in algo-seo-tfidf of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_input.json
- references/bm25-comparison.md
- references/inverted-index.md
- scripts/tfidf.py
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo SEO Tfidf 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 SEO Tfidf this skillasgard-ai-platform/skills | 242 | — | ~953 | Automated safety check: Pass | MIT | |
| Firecrawl Search Integrationfirecrawl/firecrawl | 190k | 1 repos | ~1.1k | Automated safety check: Pass | ISC | |
| Product Full-Text Searchlobehub/lobehub | 83k | — | ~4.1k | Automated safety check: Pass | Custom licence | |
| Project Orchestratorthis-rs/project-orchestrator | 140 | — | ~2.6k | Automated safety check: Pass | Custom licence | |
| Hackernewssigcli/sigcli | 293 | — | ~2k | Automated safety check: Pass | MIT | |
| Create Skillxlanex6/nuxt-meilisearch | 123 | — | ~752 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algo SEO Tfidf
What does Algo SEO Tfidf do?
Implement TF-IDF scoring to measure term importance relative to a document corpus. Algo SEO Tfidf is an agent skill from asgard-ai-platform/skills. Implement TF-IDF scoring to measure term importance relative to a document corpus.
When should I use Algo SEO Tfidf?
Algo SEO Tfidf fits situations like: the user needs to rank documents by keyword relevance; extract important terms from text; build a basic search relevance engine — even if they say find relevant documents; keyword extraction.
How do I install Algo SEO Tfidf in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-seo-tfidf -a claude-code`. Or copy the skill folder (algo-seo-tfidf in asgard-ai-platform/skills) into .claude/skills/algo-seo-tfidf in your project. Claude Code loads it when a task matches its description.
How do I install Algo SEO Tfidf in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-seo-tfidf -a codex`. Or copy the skill folder (algo-seo-tfidf in asgard-ai-platform/skills) into .agents/skills/algo-seo-tfidf in your project. Codex loads it when a task matches its description.
Can I use Algo SEO Tfidf 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-seo-tfidf -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-seo-tfidf, .gemini/skills/algo-seo-tfidf, .github/skills/algo-seo-tfidf and .opencode/skills/algo-seo-tfidf in your project.
What does Algo SEO Tfidf need to run?
Going by SKILL.md and its folder, Algo SEO Tfidf needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Algo SEO Tfidf 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 SEO Tfidf 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Algo SEO Tfidf use?
Algo SEO Tfidf 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 SEO Tfidf use?
About 953 tokens (SKILL.md is roughly 3.8k 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 5.8k tokens, read only when the agent opens those files.
What are the alternatives to Algo SEO Tfidf?
Skills that share tags, products or a category with Algo SEO Tfidf: Firecrawl Search Integration (firecrawl/firecrawl, 190k stars), Product Full-Text Search (lobehub/lobehub, 83k stars), Project Orchestrator (this-rs/project-orchestrator, 140 stars) and Hackernews (sigcli/sigcli, 293 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo SEO Tfidf?
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.