Import
asgeirtj/system_prompts_leaks
Handle explicit /import requests for read-only transcript recovery and a resume checkpoint, or continue work from other coding agents and unnamed artifacts.
Implement PageRank algorithm to compute web page importance scores using the random surfer model.
MITAuto-check passed
Install Algo SEO Pagerank
$ npx skills add asgard-ai-platform/skills --skill algo-seo-pagerank -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-pagerank --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-pagerank .claude/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .claude/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerankType 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-pagerank -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-pagerank --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-pagerank .agents/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .agents/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerank -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-pagerank --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-pagerank .cursor/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .cursor/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerank--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-pagerank -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-seo-pagerank --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-pagerank .gemini/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .gemini/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerankInstalls 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-pagerank -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-pagerank .github/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .github/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerank -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-pagerank --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-pagerank .opencode/skills/algo-seo-pagerank && 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-pagerank" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-seo-pagerank into .opencode/skills/algo-seo-pagerank/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-seo-pagerank", 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-pagerank- GitHub stars
- 242
- Token cost
- ~924 tokens
- SKILL.md length
- 384 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Implement PageRank algorithm to compute web page importance scores using the random surfer model.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to rank pages by link authority
- 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 SEO Pagerank is an agent skill from asgard-ai-platform/skills. Implement PageRank algorithm to compute web page importance scores using the random surfer model. Use this skill when the user needs to rank pages by link authority, build a simplified search ranking system, or understand how link structure determines page importance — even if they say 'which pages are most important', 'link analysis', or 'page authority score'.
Its SKILL.md is about 920 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/convergence-proof.md` and `references/sparse-implementation.md`).
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 pages by link authority
- Build a simplified search ranking system
- Understand how link structure determines page importance — even if they say which pages are most important
- Page authority score
Example prompts
- “which pages are most important”
- “link analysis”
- “page authority score”
- “/algo-seo-pagerank”
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 SEO Pagerank loads about 924 tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 384 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). 384 words, ~924 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-seo-pagerank/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-seo-pagerank
- description
- Implement PageRank algorithm to compute web page importance scores using the random surfer model. Use this skill when the user needs to rank pages by link authority, build a simplified search ranking system, or understand how link structure determines page importance — even if they say 'which pages are most important', 'link analysis', or 'page authority score'.
- metadata.category
- WP-35 SEO 演算法
- metadata.tags
- seo, pagerank, graph-algorithm, link-analysis
PageRank Algorithm
Overview
PageRank computes the importance of web pages by modeling a random surfer who follows links with probability d (damping factor) and jumps to a random page with probability 1-d. Converges in O(k * E) where k is iterations and E is number of edges.
When to Use
Trigger conditions:
- Computing page importance from link graph structure
- Building link-based authority scoring systems
- Analyzing citation networks or any directed graph importance
When NOT to use:
- When you only need keyword relevance (use TF-IDF instead)
- When the graph is undirected or unweighted (consider centrality measures)
Algorithm
IRON LAW: PageRank Convergence
- Damping factor d MUST be < 1 (typically 0.85)
- Without damping, rank sinks and spider traps break convergence
- Correctness invariant: sum of all PageRank values = 1.0Phase 1: Input Validation
Build adjacency list from link data. Verify: no self-loops counted, all nodes accounted for (including dangling nodes with no outlinks). Gate: Graph is well-formed, dangling nodes identified.
Phase 2: Core Algorithm
- Initialize all N pages with PR = 1/N
- For each iteration:
- For each page p: PR(p) = (1-d)/N + d * Σ(PR(q)/L(q)) for all q linking to p
- Distribute dangling node rank equally to all pages
- Repeat until convergence (L1 norm change < ε, typically 1e-6)
Phase 3: Verification
Check: all PR values sum to ~1.0. Compare top-k rankings against known authority pages. Gate: |Σ PR - 1.0| < 0.001 and convergence achieved within max iterations.
Phase 4: Output
Return sorted page scores with rank position.
Output Format
{
"rankings": [{"page": "url", "score": 0.042, "rank": 1}],
"metadata": {"nodes": 1000, "edges": 5000, "iterations": 45, "damping": 0.85, "converged": true}
}Examples
Sample I/O
Input: Pages A→B, A→C, B→C, C→A (3 nodes, 4 edges, d=0.85) Expected Output: C: 0.390, A: 0.327, B: 0.283 (approximate)
Show full SKILL.md (147 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Single node, no links | PR = 1.0 | Only node gets all rank |
| All nodes link to one | Target gets highest PR | Star topology concentrates rank |
| Dangling node (no outlinks) | Distribute its rank equally | Prevents rank leakage |
Gotchas
- Dangling nodes: Pages with no outgoing links leak rank. Redistribute their rank equally across all pages each iteration.
- Spider traps: A group of pages that only link to each other accumulate rank. Damping factor prevents this but doesn't eliminate it entirely.
- Convergence speed: Dense graphs converge faster. Sparse graphs with long chains may need 100+ iterations.
- Floating point accumulation: For large graphs, use double precision. Single precision drifts noticeably after 50+ iterations.
- Personalized PageRank: Standard PageRank uses uniform random jump. For personalized recommendations, bias the jump vector toward seed pages.
References
- For mathematical derivation of convergence proof, see
references/convergence-proof.md - For efficient sparse matrix implementation, see
references/sparse-implementation.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-seo-pagerank of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/convergence-proof.md
- references/sparse-implementation.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo SEO Pagerank 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 Pagerank this skillasgard-ai-platform/skills | 242 | — | ~924 | Automated safety check: Pass | MIT | |
| Importasgeirtj/system_prompts_leaks | 69k | — | ~3.5k | Automated safety check: Pass | CC0-1.0 | |
| Implementsickn33/agentic-awesome-skills | 47k | 5 repos | ~306 | Automated safety check: Pass | MIT | |
| Implementcodewhale-hq/Codewhale | 41k | — | ~190 | Automated safety check: Pass | MIT | |
| Ito Computeaffaan-m/ECC | 277k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Claw Scoreopenclaw/openclaw | 392k | — | ~2.5k | Automated safety check: Pass | MIT |
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Questions about Algo SEO Pagerank
What does Algo SEO Pagerank do?
Implement PageRank algorithm to compute web page importance scores using the random surfer model. Algo SEO Pagerank is an agent skill from asgard-ai-platform/skills. Implement PageRank algorithm to compute web page importance scores using the random surfer model.
When should I use Algo SEO Pagerank?
Algo SEO Pagerank fits situations like: the user needs to rank pages by link authority; build a simplified search ranking system; understand how link structure determines page importance — even if they say which pages are most important; page authority score.
How do I install Algo SEO Pagerank in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-seo-pagerank -a claude-code`. Or copy the skill folder (algo-seo-pagerank in asgard-ai-platform/skills) into .claude/skills/algo-seo-pagerank in your project. Claude Code loads it when a task matches its description.
How do I install Algo SEO Pagerank in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-seo-pagerank -a codex`. Or copy the skill folder (algo-seo-pagerank in asgard-ai-platform/skills) into .agents/skills/algo-seo-pagerank in your project. Codex loads it when a task matches its description.
Can I use Algo SEO Pagerank 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-pagerank -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-pagerank, .gemini/skills/algo-seo-pagerank, .github/skills/algo-seo-pagerank and .opencode/skills/algo-seo-pagerank in your project.
What does Algo SEO Pagerank need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo SEO Pagerank is instructions for the agent only.
Does Algo SEO Pagerank 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 Pagerank 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 SEO Pagerank use?
Algo SEO Pagerank 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 Pagerank use?
About 924 tokens (SKILL.md is roughly 3.7k 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 4.8k tokens, read only when the agent opens those files.
What are the alternatives to Algo SEO Pagerank?
Skills that share tags, products or a category with Algo SEO Pagerank: Import (asgeirtj/system_prompts_leaks, 69k stars), Implement (sickn33/agentic-awesome-skills, 47k stars), Implement (codewhale-hq/Codewhale, 41k stars) and Ito Compute (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo SEO Pagerank?
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.