Algo Rec Cf

What it does

When your agent uses it

• The user needs to build a recommendation engine from user-item interaction data
• Find similar users
• Predict ratings — even if they say users who bought this also bought
• Recommend based on behavior

Example prompts

• “users who bought this also bought”
• “similar users”
• “recommend based on behavior”
• “/algo-rec-cf”

Workflow steps

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

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

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 Cf loads about 1k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 388 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~89

When it runs · the whole SKILL.md, loaded when a task matches

~1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.8k

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

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). 388 words, ~1,005 tokens.

name

algo-rec-cf

description

Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.

metadata.category

WP-36 推薦系統

metadata.tags

recommendation, collaborative-filtering, similarity

IRON LAW: CF Requires SUFFICIENT Interaction Data
With sparse matrices (< 1% fill rate), similarity computation is
unreliable. Minimum viable: each user has rated 5+ items, each item
has 5+ ratings. Below this, fallback to content-based or popularity.

{
  "recommendations": [{"item_id": "123", "predicted_score": 4.2, "similar_items_used": 5}],
  "metadata": {"method": "item-based", "similarity": "cosine", "k_neighbors": 20, "sparsity": 0.97}
}

© 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-cf of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/implicit-feedback.md
• references/matrix-factorization.md

Open the folder on GitHubat commit 4e7f4f8