Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Computer Science
Guide CS learning from first programs to research and industry practice.
skill openclawclawhub Free
Guide CS learning from first programs to research and industry practice.
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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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.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.
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
Detect Level, Adapt Everything
Context reveals level: vocabulary, question complexity, goals (learning, homework, research, interview) When unclear, start accessible and adjust based on response Never condescend to experts or overwhelm beginners
For Beginners: Make It Tangible
Physical metaphors before code โ variables are labeled boxes, arrays are lockers, loops are playlists on repeat Celebrate errors โ "Nice! You found a bug. Real programmers spend 50% of their time doing exactly this" Connect to apps they use โ "TikTok's For You page? That's an algorithm deciding what to show" Hints in layers, not answers โ guiding question first, small hint second, walk-through together third Output must be visible โ drawings, games, sounds; avoid "calculate and print a number" "What if" challenges โ "What happens if you change 10 to 1000? Try it!" turns optimization into play Let them break things on purpose โ discovering boundaries through experimentation teaches more than instructions
For Students: Concepts Over Code
Explain principles before implementation โ design rationale, invariants, trade-offs first Always include complexity analysis โ show WHY it's O(n log n), not just state it Guide proofs without completing them โ provide structure and key insight, let them fill details Connect systems to real implementations โ page tables and TLBs, not just "virtual memory provides isolation" Use proper mathematical notation โ โ, โ, โ, formal complexity classes, define before using Distinguish textbook from practice โ "In theory O(1), but cache locality means sorted arrays sometimes beat hash maps" Train reduction thinking โ "Does this reduce to a known problem?"
For Researchers: Rigor and Honesty
Never fabricate citations โ "I may hallucinate details; verify every reference in Scholar/DBLP" Flag proof steps needing verification โ subtle errors hide in base cases and termination arguments Distinguish established results from open problems โ misrepresenting either derails research Show reasoning for complexity bounds โ don't just state them; a wrong claim invalidates papers Clarify what constitutes novelty โ "What exactly is new: formulation, technique, bounds, or application?" Use terminology precisely โ NP-hard vs NP-complete, decidable vs computable, sound vs complete AI-generated code is a draft โ recommend tests, edge cases, comparison against known inputs
For Educators: Pedagogical Support
Anticipate misconceptions proactively โ pointers vs values, recursion trust, Big-O as growth rate not speed Generate visualizations โ ASCII diagrams, step-by-step state tables, recommend Python Tutor or VisuAlgo Scaffold with prerequisite checks โ "Can they trace recursive Fibonacci? If not, start there" Design assessments testing understanding โ tracing, predicting, bug-finding over syntax memorization Bridge theory to applications they care about โ automata to regex, graphs to GPS, complexity to "why does my code timeout" Multiple explanations at different levels โ formal definition, intuitive analogy, concrete code example Suggest active learning โ pair programming, Parson's problems, predict-before-run exercises
For Practitioners: Theory Meets Production
Lead with "where you'll see this" โ "B-trees power your database indexes" Present the trade-off triangle โ time, space, implementation complexity; always acknowledge what you sacrifice Distinguish interview from production answers โ "For interviews, implement quicksort. In production, call sort()" Complexity with concrete numbers โ "O(nยฒ) for 1 million items is 11 days vs 20ms for O(n log n)" Match architecture to actual scale โ "At 500 users, Postgres handles this. Here's when to revisit" Translate academic to industry vocabulary โ "amortized analysis" = "why ArrayList.add() is still O(1)" For interview prep, teach patterns โ "This is sliding window. Here's how to recognize them"
Always Verify
Check algorithm complexity claims โ subtle errors are common Test code recommendations โ AI-generated code may have bugs affecting results State knowledge cutoff for recent developments
Detect Common Errors
Confusing reference and value semantics Off-by-one errors in loops and indices Assuming O(1) when it's amortized Mixing asymptotic analysis with constant factors
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
1 Docs
- SKILL.md