Review Ugc Render
gooseworks-ai/goose-skills
Mandatory pre-publish review gate for a UGC video render. An agent skill from gooseworks-ai/goose-skills.
Solve the influence maximization problem to select optimal seed nodes for maximum information spread.
Install Algo Net Influence
$ npx skills add asgard-ai-platform/skills --skill algo-net-influence -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-net-influence --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-net-influence .claude/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .claude/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influenceType 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-net-influence -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-net-influence --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-net-influence .agents/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .agents/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influence -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-net-influence --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-net-influence .cursor/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .cursor/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influence--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-net-influence -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-net-influence --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-net-influence .gemini/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .gemini/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influenceInstalls 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-net-influence -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-net-influence .github/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .github/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influence -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-net-influence --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-net-influence .opencode/skills/algo-net-influence && 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-net-influence" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-net-influence into .opencode/skills/algo-net-influence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-net-influence", 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-net-influence- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 423 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Solve the influence maximization problem to select optimal seed nodes for maximum information spread.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to choose seed users for viral campaigns
- 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 Net Influence is an agent skill from asgard-ai-platform/skills. Solve the influence maximization problem to select optimal seed nodes for maximum information spread. Use this skill when the user needs to choose seed users for viral campaigns, maximize network reach under a budget constraint, or compare seeding strategies — even if they say 'who should we seed first', 'maximize viral reach', or 'optimal influencer selection'.
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/celf-implementation.md` and `references/scalable-im.md`).
It sits in Marketing & SEO, covering Influencer and creator marketing. 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 choose seed users for viral campaigns
- Maximize network reach under a budget constraint
- Compare seeding strategies — even if they say who should we seed first
- Maximize viral reach
Example prompts
- “who should we seed first”
- “maximize viral reach”
- “optimal influencer selection”
- “/algo-net-influence”
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 Net Influence loads about 1.1k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 423 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). 423 words, ~1,128 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-net-influence/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-net-influence
- description
- Solve the influence maximization problem to select optimal seed nodes for maximum information spread. Use this skill when the user needs to choose seed users for viral campaigns, maximize network reach under a budget constraint, or compare seeding strategies — even if they say 'who should we seed first', 'maximize viral reach', or 'optimal influencer selection'.
- metadata.category
- WP-46 網絡演算法
- metadata.tags
- network, influence-maximization, seed-selection, viral-marketing
Influence Maximization
Overview
Influence maximization selects k seed nodes in a network to maximize expected spread under a diffusion model (Independent Cascade or Linear Threshold). NP-hard, but the greedy algorithm achieves (1-1/e) ≈ 63% approximation guarantee due to submodularity. Practical for networks up to millions of nodes with CELF optimization.
When to Use
Trigger conditions:
- Selecting k influencers/users to seed a viral marketing campaign
- Maximizing information spread under a fixed budget (k seeds)
- Comparing seeding strategies (degree-based vs greedy vs random)
When NOT to use:
- When measuring existing influence (use centrality metrics)
- For community structure analysis (use community detection)
Algorithm
IRON LAW: Greedy With Lazy Evaluation (CELF) Is the Practical Standard
The naive greedy algorithm requires O(k × n × R) simulations where
R = Monte Carlo runs (10,000+). CELF exploits submodularity to skip
unnecessary evaluations, achieving 700x speedup. Always use CELF
over naive greedy. Simple heuristics (top-k by degree) are fast
but can perform 50%+ worse than greedy.Phase 1: Input Validation
Build network graph. Choose diffusion model: Independent Cascade (probability per edge) or Linear Threshold (threshold per node). Set k (number of seeds) and propagation probabilities. Gate: Graph loaded, diffusion model selected, k defined.
Phase 2: Core Algorithm
Greedy with CELF:
- Initialize: seed set S = ∅
- For each candidate node, estimate marginal gain: σ(S∪{v}) - σ(S) via Monte Carlo simulation (R=10,000 runs)
- Select node with highest marginal gain, add to S
- CELF optimization: reuse previous marginal gains, only re-evaluate when a node's upper bound exceeds current best
- Repeat until |S| = k
Phase 3: Verification
Compare greedy result against baselines: random seeds, top-k degree, top-k PageRank. Greedy should significantly outperform. Gate: Greedy spread > degree heuristic spread, difference is meaningful.
Phase 4: Output
Return seed set with expected spread and comparison.
Output Format
{
"seeds": [{"node": "user_42", "marginal_gain": 150, "selection_order": 1}],
"expected_spread": 2500,
"baselines": {"random": 800, "top_degree": 1900, "greedy": 2500},
"metadata": {"k": 10, "model": "independent_cascade", "mc_simulations": 10000, "nodes": 50000}
}Examples
Sample I/O
Input: Social network 10K nodes, k=5 seeds, IC model with p=0.1 per edge Expected: Greedy selects diverse, well-positioned seeds (not all high-degree), expected spread ~500-1000.
Show full SKILL.md (159 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| k=1 | Node with highest individual spread | Single seed, no overlap consideration |
| k > number of communities | One seed per community optimal | Diversity beats concentration |
| Very sparse graph (low p) | Small spread regardless of seeds | Network can't propagate with low probability |
Gotchas
- Monte Carlo variance: With R=1000, spread estimates have ~5% variance. Use R=10,000+ for stable results, especially when comparing close candidates.
- Diffusion model choice matters: IC and LT produce different optimal seed sets. IC favors high-degree nodes; LT favors nodes that can trigger cascades.
- Propagation probability estimation: Real-world edge probabilities are unknown. Common approaches: uniform (p=0.01-0.1), weighted inverse degree (1/in-degree), or learned from cascade data.
- Overlap penalty: Greedy naturally handles overlap (submodularity). Heuristics that independently select top nodes waste seeds on overlapping influence spheres.
- Scalability: Even with CELF, millions of nodes require further approximation (sketch-based methods like IMM or TIM+).
References
- For CELF and CELF++ implementation, see
references/celf-implementation.md - For scalable influence maximization (IMM), see
references/scalable-im.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-net-influence of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/celf-implementation.md
- references/scalable-im.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Net Influence 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 Net Influence this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Review Ugc Rendergooseworks-ai/goose-skills | 1.2k | — | ~3.4k | Automated safety check: Pass | MIT | |
| Audience ResearchScrapeCreators/social-media-research-skills | 3.4k | — | ~635 | Automated safety check: Notes | MIT | |
| Influencer Discoverytigerless-labs/influencer-discovery | 212 | — | ~2.5k | Automated safety check: Notes | None | |
| Opencloneteam-attention/openclone | 130 | — | ~2.6k | Automated safety check: Notes | MIT | |
| Reelclaw Adsdansugc/reelclaw | 145 | — | ~3.9k | Automated safety check: Notes | MIT |
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Related
Categories
Questions about Algo Net Influence
What does Algo Net Influence do?
Solve the influence maximization problem to select optimal seed nodes for maximum information spread. Algo Net Influence is an agent skill from asgard-ai-platform/skills. Solve the influence maximization problem to select optimal seed nodes for maximum information spread.
When should I use Algo Net Influence?
Algo Net Influence fits situations like: the user needs to choose seed users for viral campaigns; maximize network reach under a budget constraint; compare seeding strategies — even if they say who should we seed first; maximize viral reach.
How do I install Algo Net Influence in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-net-influence -a claude-code`. Or copy the skill folder (algo-net-influence in asgard-ai-platform/skills) into .claude/skills/algo-net-influence in your project. Claude Code loads it when a task matches its description.
How do I install Algo Net Influence in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-net-influence -a codex`. Or copy the skill folder (algo-net-influence in asgard-ai-platform/skills) into .agents/skills/algo-net-influence in your project. Codex loads it when a task matches its description.
Can I use Algo Net Influence 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-net-influence -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-net-influence, .gemini/skills/algo-net-influence, .github/skills/algo-net-influence and .opencode/skills/algo-net-influence in your project.
What does Algo Net Influence need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Net Influence is instructions for the agent only.
Does Algo Net Influence 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 Net Influence 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 Net Influence use?
Algo Net Influence 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 Net Influence use?
About 1.1k tokens (SKILL.md is roughly 4.5k 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 Net Influence?
Skills that share tags, products or a category with Algo Net Influence: Review Ugc Render (gooseworks-ai/goose-skills, 1.2k stars), Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars), Influencer Discovery (tigerless-labs/influencer-discovery, 212 stars) and Openclone (team-attention/openclone, 130 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Net Influence?
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.