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Agent skill

Algo Rec Content

by asgard-ai-platform in asgard-ai-platform/skills

Implement content-based recommendation by matching item features to user preference profiles.

MITAuto-check passed

Install Algo Rec Content

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-rec-content -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-rec-content --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-rec-content .claude/skills/algo-rec-content && rm -rf skills-src

Use ~/.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/

Facts

Skill name
algo-rec-content
GitHub stars
242
Token cost
~980 tokens
SKILL.md length
371 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Implement content-based recommendation by matching item features to user preference profiles.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to recommend items based on attributes
  • 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 Rec Content is an agent skill from asgard-ai-platform/skills. Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.

Its SKILL.md is about 980 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/feature-extraction.md` and `references/hybrid-strategies.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 recommend items based on attributes
  • Solve the cold start problem for new items
  • Build recommendations without collaborative data — even if they say recommend similar products
  • Items like this

Example prompts

  • “recommend similar products”
  • “items like this”
  • “feature-based matching”
  • “/algo-rec-content”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

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 Rec Content loads about 980 tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 371 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~980
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.7k

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). 371 words, ~980 tokens.

Download SKILL.mdSave it as .claude/skills/algo-rec-content/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-rec-content
description
Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.
metadata.category
WP-36 推薦系統
metadata.tags
recommendation, content-based, feature-matching

Content-Based Recommendation

Overview

Content-based filtering recommends items whose features match the user's preference profile, built from their interaction history. Computes in O(I × F) per user where I=items, F=features. Solves new-item cold start since items only need features, not interaction history.

When to Use

Trigger conditions:

  • Recommending based on item attributes (genre, category, keywords, price range)
  • New item cold start: items have features but no interaction data yet
  • When user privacy requires no cross-user data sharing

When NOT to use:

  • When serendipity matters (content-based creates filter bubbles)
  • When item features are unavailable or uninformative (use CF instead)

Algorithm

IRON LAW: Content-Based Can Only Recommend SIMILAR Items
It cannot discover unexpected interests (filter bubble problem).
Users who only interact with action movies will only get action
movie recommendations — even if they'd love a documentary.
Phase 1: Input Validation

Extract item feature vectors (TF-IDF for text, one-hot for categories, numerical for attributes). Build user profile from weighted item features of interacted items. Gate: Item features extracted, user profile vector built.

Phase 2: Core Algorithm
  1. Represent each item as a feature vector
  2. Build user profile: weighted centroid of interacted item vectors (weight by recency, rating, or engagement)
  3. Compute similarity between user profile and all candidate items (cosine similarity)
  4. Rank by similarity score, exclude already-interacted items
Phase 3: Verification

Evaluate: does the recommendation list reflect the user's demonstrated preferences? Check diversity metrics. Gate: Recommendations are topically aligned with user history.

Phase 4: Output

Return ranked recommendations with feature-level explanations.

Output Format

json
{
  "recommendations": [{"item_id": "456", "score": 0.87, "matching_features": ["genre:thriller", "director:Nolan"]}],
  "metadata": {"method": "content-based", "features_used": 15, "profile_items": 30}
}

Examples

Show full SKILL.md (155 more words)Show less
Sample I/O

Input: User watched 5 sci-fi movies, 2 documentaries. Candidate: new sci-fi movie. Expected: High score (~0.8+) due to genre match with dominant preference.

Edge Cases
InputExpectedWhy
New user, no historyCannot build profileNew-user cold start — use popularity
All items same featuresEqual scoresNo differentiation possible
User with diverse historyModerate scores for allProfile averages dilute signal

Gotchas

  • Feature quality is everything: Garbage features → garbage recommendations. Invest in feature engineering.
  • Filter bubble: Users get increasingly narrow recommendations. Inject diversity by mixing in exploration items.
  • Profile drift: User preferences change over time. Apply temporal decay to older interactions.
  • Feature sparsity: Items with few features produce unreliable similarity. Set a minimum feature count threshold.
  • Over-specialization: A user who rated one jazz album highly shouldn't get ALL jazz. Weight by interaction count, not just rating.

References

  • For hybrid approaches combining content and CF, see references/hybrid-strategies.md
  • For text-based feature extraction techniques, see references/feature-extraction.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-rec-content of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/feature-extraction.md
  • references/hybrid-strategies.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Rec Content 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.

Algo Rec Content compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Algo Rec Content this skillasgard-ai-platform/skills242—~980Automated safety check: PassMIT
Implementsickn33/agentic-awesome-skills47k5 repos~306Automated safety check: PassMIT
Implementcodewhale-hq/Codewhale41k—~190Automated safety check: PassMIT
Incremental Implementationaddyosmani/agent-skills105k1 repos~2.3kAutomated safety check: PassMIT
Implementbestofjs/bestofjs3.1k18 repos~109Automated safety check: PassMIT
ImplementAutomattic/simplenote-android1.9k—~1.1kAutomated safety check: PassGPL-2.0

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Questions about Algo Rec Content

What does Algo Rec Content do?

Implement content-based recommendation by matching item features to user preference profiles. Algo Rec Content is an agent skill from asgard-ai-platform/skills. Implement content-based recommendation by matching item features to user preference profiles.

When should I use Algo Rec Content?

Algo Rec Content fits situations like: the user needs to recommend items based on attributes; solve the cold start problem for new items; build recommendations without collaborative data — even if they say recommend similar products; items like this.

How do I install Algo Rec Content in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-rec-content -a claude-code`. Or copy the skill folder (algo-rec-content in asgard-ai-platform/skills) into .claude/skills/algo-rec-content in your project. Claude Code loads it when a task matches its description.

How do I install Algo Rec Content in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-rec-content -a codex`. Or copy the skill folder (algo-rec-content in asgard-ai-platform/skills) into .agents/skills/algo-rec-content in your project. Codex loads it when a task matches its description.

Can I use Algo Rec Content 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-rec-content -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-rec-content, .gemini/skills/algo-rec-content, .github/skills/algo-rec-content and .opencode/skills/algo-rec-content in your project.

What does Algo Rec Content need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Rec Content is instructions for the agent only.

Does Algo Rec Content 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 Rec Content 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 Rec Content use?

Algo Rec Content 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 Rec Content use?

About 980 tokens (SKILL.md is roughly 3.9k 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 4.7k tokens, read only when the agent opens those files.

What are the alternatives to Algo Rec Content?

Skills that share tags, products or a category with Algo Rec Content: Implement (sickn33/agentic-awesome-skills, 47k stars), Implement (codewhale-hq/Codewhale, 41k stars), Incremental Implementation (addyosmani/agent-skills, 105k stars) and Implement (bestofjs/bestofjs, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Rec Content?

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.