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Recommend

Context-aware recommendations. Learns preferences, researches options, anticipates expectations.

skill openclawclawhub Free

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High Signal

Context-aware recommendations. Learns preferences, researches options, anticipates expectations.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, categories.md, sources.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ivangdavila
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 8 sections Open source page

Core Loop

Context → Preferences → Research → Match → Recommend Every recommendation requires: knowing the user + knowing the options. Check sources.md for where to find user context. Check categories.md for domain-specific factors.

Step 1: Context Gathering

Before recommending, search user context. See sources.md for full source list. Minimum output: 3-5 relevant user signals before proceeding. If insufficient, ask targeted questions.

Step 2: Preference Extraction

From gathered context, extract: DimensionQuestionValuesWhat matters most? (Quality, price, speed, novelty, safety)ConstraintsHard limits? (Budget, time, dietary, ethical)HistoryWhat worked? What disappointed?MoodAdventurous or safe? Exploring or comfort? Output: 3-5 bullet preference profile for this request.

Step 3: Research Options

Now—and only now—research candidates: Breadth first: Don't anchor on first good option Source quality: Prioritize reviews, ratings, expert opinions Recency: Check if information is current Availability: Confirm options are actually accessible Output: Shortlist of 3-7 viable candidates with key attributes.

Step 4: Match & Rank

Score each candidate against the preference profile: Candidate → Values alignment + Constraint fit + History match + Mood fit Disqualify anything that violates hard constraints. Rank by total alignment, not just one dimension.

Step 5: Recommend

Present 1-3 recommendations: 🎯 RECOMMENDATION: [Option] 📌 WHY: Matches [preference], avoids [constraint] ⚖️ TRADEOFF: Less [X] than [Alternative] 🔍 CONFIDENCE: [Level] — based on [data quality]

Adaptive Learning

After each recommendation: Track outcome: Accepted? Modified? Rejected? Update preferences: Acceptance = reinforcement, rejection = adjustment Note exceptions: "Normally X, but for Y context preferred Z" Store learnings in memory for future recommendations.

Traps

Projecting — Your taste ≠ their taste Recency bias — Last choice isn't always preference Ignoring context — Tuesday lunch ≠ anniversary dinner Over-filtering — Too many constraints = nothing fits Stale data — Preferences evolve, verify periodically Recommendations are predictions. More context = better predictions.

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs

SKILL.md Primary doc
categories.md Docs
sources.md Docs