Tencent SkillHub · AI

Prompting

Write, test, and iterate prompts for AI models with voice preservation, model-specific adaptation, and systematic failure analysis.

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High Signal

Write, test, and iterate prompts for AI models with voice preservation, model-specific adaptation, and systematic failure analysis.

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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.

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, failures.md, iteration.md, memory-template.md, models.md, techniques.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.

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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 12 sections Open source page

Architecture

Prompt patterns and user preferences live in ~/prompting/. ~/prompting/ ├── memory.md # HOT: user voice, model preferences, learned corrections ├── patterns/ # Reusable prompt templates by task type └── history.md # Past prompts with outcomes See memory-template.md for initial setup.

Quick Reference

TopicFileCommon failure modesfailures.mdModel-specific quirksmodels.mdIteration workflowiteration.mdAdvanced techniquestechniques.md

1. Ask Before Assuming

Before writing any prompt, ask: What model? (GPT-4, Claude, Haiku, Gemini) What's the failure mode you're seeing? (if iterating) Token budget? (cost-sensitive vs. quality-first) Never default to verbose. Simpler often wins.

2. Preserve What Works

When improving a failing prompt: Change ONE thing at a time Note what's currently working Surgical fixes > rewrites

3. Model-Specific Adaptation

See models.md — key differences: Claude: explicit constraints, less scaffolding needed GPT-4: benefits from step-by-step, tolerates verbose Haiku/fast models: brevity critical, skip examples when possible Prompt optimized for one model will underperform on others.

4. Voice Lock

When user provides writing samples: Extract specific patterns (sentence length, punctuation, vocabulary) Apply consistently throughout session Check output against samples before delivering

5. True Variation

When generating alternatives, vary: Structure (not just synonyms) Emotional angle Opening hook Call-to-action style "Top 5 reasons" → "The hidden truth about" → "What nobody tells you about" = real variation.

6. Failure Classification

When a prompt fails, classify the failure type: Hallucination → add grounding, sources, constraints Format break → strengthen output spec, add examples Instruction drift → move critical constraints earlier Refusal → rephrase intent, remove ambiguity Different failures need different fixes. See failures.md.

7. Compression Bias

Default to removing words, not adding. Test: "Does removing this line change the output?" If no, remove. Token costs matter. A prompt that works with 50 tokens beats one that needs 500.

8. Test Case Generation

When asked to test a prompt: Generate edge cases (empty input, very long, special chars) Include adversarial inputs Test boundary conditions Don't just test happy path.

9. Platform-Native Output

For content prompts, know platform constraints: Twitter: 280 chars, no markdown LinkedIn: longer ok, hashtags matter Instagram: emoji-friendly, visual hooks Prompt should enforce format, not hope for it.

10. Memory Persistence

Store in ~/prompting/memory.md: User's preferred style (terse vs detailed) Target models they commonly use Past corrections ("I told you I don't want emojis") Reference before every prompting task.

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

6 Docs

SKILL.md Primary doc
failures.md Docs
iteration.md Docs
memory-template.md Docs
models.md Docs
techniques.md Docs