Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Agentic Coding
Ship production code with AI agents through acceptance contracts, micro diffs, red green loops, and deterministic handoff checkpoints.
skill openclawclawhub Free
Ship production code with AI agents through acceptance contracts, micro diffs, red green loops, and deterministic handoff checkpoints.
β¬ 0 downloads β
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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, handoff.md, memory-template.md, prompt-contracts.md, protocol.md, setup.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
Setup
If ~/agentic-coding/ does not exist or is empty, read setup.md, ask a concise kickoff question, and keep any persistence explicitly opt-in.
Positioning
This skill is intentionally different from agentic-engineering and vibe-coding: agentic-engineering focuses on multi-agent operating patterns and team throughput. vibe-coding focuses on prompt-led exploration and fast idea shipping. agentic-coding focuses on contract-first implementation, proof of fix, and reviewer-ready handoff.
When to Use
User needs merge-ready code from an AI agent with explicit quality gates. Use for production features, risky refactors, bug fixes with reproducible failures, and Xcode-centered work such as Swift feature delivery, iOS/macOS regressions, and release-branch hotfixes.
Architecture
Memory lives in ~/agentic-coding/. See memory-template.md for setup. ~/agentic-coding/ |- memory.md # Persistent preferences and operating mode |- contracts.md # Accepted task contracts and non-goals |- evidence.md # Test evidence and verification snapshots `- handoffs.md # Delivery notes and rollback hints
Quick Reference
Load these files on demand to keep context focused and execution fast. TopicFileSetup processsetup.mdMemory templatememory-template.mdPACT loopprotocol.mdContract promptsprompt-contracts.mdMerge handoff checklisthandoff.md
1. Lock a Contract Before Writing Code
Start every task with a compact contract: Objective: exact outcome in one sentence Acceptance: checks that prove success Non-goals: what must stay untouched Constraints: stack, style, limits, deadlines No contract, no code.
2. Run the PACT Loop
Use the same execution loop every time: Problem framing: restate objective and assumptions Acceptance design: define checks before edits Change set: produce the smallest useful diff Trace and test: show evidence and residual risk This skill is execution discipline, not brainstorming. For Xcode workflows, tie acceptance to a concrete target, simulator/device, and test command before editing.
3. Keep Diffs Surgical
One user objective maps to one focused change set: Prefer file-local edits over broad rewrites Separate behavior change from style cleanup Avoid hidden side effects outside declared scope If scope grows, split into a second contract.
4. Prove Failure Then Prove Fix
For bugs and regressions: Capture the failing condition first (test, log, or reproduction) Apply minimal fix Re-run the same check to prove resolution Never claim fixed without before and after evidence.
5. Deliver Handoff-Grade Output
End each cycle with a delivery packet: What changed and why Files touched and blast radius Validation run and results Known risks and rollback path If handoff is unclear, the task is not finished.
6. Escalate With a Structured Fallback
When blocked after two failed attempts: Stop editing State what was tried Propose two grounded alternatives Request a decision with tradeoffs Do not keep guessing in loops.
Common Traps
Starting implementation without acceptance checks -> endless iteration and unclear done state. Asking the agent for full rewrites -> noisy diffs and avoidable regressions. Mixing feature work with architecture overhaul -> weak reviewability and hard rollback. Reporting success without reproducible evidence -> false confidence in production. Treating AI output as final draft -> quality debt moved to code review.
Security & Privacy
Data that leaves your machine: None from this skill itself Data that stays local: Contracts, evidence notes, and handoff summaries in ~/agentic-coding/ This skill does NOT: Trigger undeclared network requests Access files outside its own memory path Write to global or platform memory stores Auto-approve risky code without explicit evidence
Scope
This skill ONLY: Improves execution quality of AI-assisted coding Enforces contract driven implementation and verification Produces clear handoff packets for reviewers This skill NEVER: Replaces security review for high risk domains Encourages blind trust in generated code Overrides project specific contribution rules
Related Skills
Install with clawhub install <slug> if user confirms: agentic-engineering - Multi-agent collaboration and operating patterns. coding - General coding support across stacks and tasks. code - Broad code authoring and editing assistance. copilot - Companion style IDE assistance patterns. delegate - Structured task delegation to autonomous agents.
Feedback
If useful: clawhub star agentic-coding Stay updated: clawhub sync
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
- handoff.md
- memory-template.md
- prompt-contracts.md
- protocol.md
- setup.md