Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Scale Frameworks
Scale systems, software architecture, and companies with bottleneck mapping, staged leverage plans, and risk-aware execution loops.
skill openclawclawhub Free
Scale systems, software architecture, and companies with bottleneck mapping, staged leverage plans, and risk-aware execution loops.
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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, architecture-scale-framework.md, company-scale-framework.md, execution-cadence.md, memory-template.md, scale-diagnostic.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
On first use, read setup.md for integration and activation guidance.
When to Use
Use this skill when the user wants to scale something with real constraints: technical systems, software architecture, organizations, operations, or go-to-market capacity. The skill applies the same core logic across domains: find the bottleneck, select the smallest high-leverage move, and verify with explicit guardrails before expanding. This skill is advisory and planning-focused. It does not run infrastructure changes, reorganize teams, or execute live migrations without user confirmation and domain tooling.
Architecture
Memory lives in ~/scale/. See memory-template.md for structure and status fields. ~/scale/ |- memory.md # Durable scaling context and activation preferences |- bottleneck-map.md # Active constraints and bottleneck hypotheses |- leverage-backlog.md # Candidate changes ranked by impact and effort `- experiment-log.md # Outcomes, regressions, and rollout notes
Quick Reference
Use the smallest relevant file for the current scaling problem. TopicFileSetup and integrationsetup.mdMemory structure and statesmemory-template.mdUniversal intake and bottleneck diagnosisscale-diagnostic.mdInfrastructure and platform scalingsystem-scale-framework.mdSoftware architecture scalingarchitecture-scale-framework.mdTeam and business scalingcompany-scale-framework.mdCadence, metrics, and rollout controlexecution-cadence.md
1. Define Scale Target Before Solutions
Always lock these inputs first: What must scale: throughput, reliability, team output, revenue, or customer base Time horizon: immediate, quarter, or year Non-negotiable constraints: budget, compliance, headcount, latency, quality No target, no valid scaling plan.
2. Work the BOLT Loop
For every scaling request, apply BOLT in order: Bottleneck: identify the dominant limiting factor now Objective: define measurable win condition Levers: list 3 to 5 candidate interventions Test: run staged validation with rollback criteria Do not skip directly from symptoms to large transformations.
3. Prioritize Smallest Effective Change
Default to interventions that unlock capacity fast with bounded risk: Remove queueing friction before adding complexity Improve interfaces and ownership before splitting services Standardize repeated work before hiring aggressively Big rewrites are last resort, not default strategy.
4. Price Second-Order Effects Explicitly
Each recommendation must include likely side effects: New failure modes Cost and operational overhead growth Coordination load across teams Risk of local optimization hurting global performance If second-order risk is unknown, mark as hypothesis and constrain rollout.
5. Pair Every KPI with a Guardrail
Never scale on a single growth metric. Pair it with guardrails: Throughput with error rate Deploy velocity with change failure rate Sales growth with gross margin and support load If guardrails degrade, pause expansion and stabilize.
6. Separate Temporary Boosts from Durable Capacity
Label every action as one of two types: Temporary boost: overtime, manual review, tactical exceptions Durable capacity: automation, architecture simplification, reusable process Use temporary boosts only to buy time for durable capacity.
7. Institutionalize What Works
After each successful change: Capture trigger conditions Document operating playbook and owner Add review cadence and retirement criteria Scaling compounds only when wins become repeatable systems.
Common Traps
Hiring before workflow clarity -> headcount increases coordination drag. Splitting monoliths before interface discipline -> distributed outages with slower delivery. Scaling traffic without SLO guardrails -> growth hides reliability collapse. Copying big-company org charts too early -> decision latency and ownership gaps. Optimizing one bottleneck in isolation -> next bottleneck shifts and total flow does not improve. Confusing activity with throughput -> teams look busy while output stagnates.
Security & Privacy
Data that leaves your machine: None by default from this skill itself. Data that stays local: Scaling context and learned operating patterns under ~/scale/. This skill does NOT: Execute undeclared network requests automatically. Apply irreversible technical or organizational changes without explicit user approval. Store secrets, credentials, or payment data in local memory files. Modify files outside ~/scale/ for memory storage.
Related Skills
Install with clawhub install <slug> if user confirms: architecture - Architectural fundamentals and constraints that shape scaling decisions. systems-architect - Reliability, infrastructure, and platform tradeoff patterns. startup - Stage-aware startup execution and prioritization logic. growth - Demand generation and growth loops once capacity is ready. strategy - Strategic framing and tradeoff analysis across long horizons.
Feedback
If useful: clawhub star scale 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
- architecture-scale-framework.md
- company-scale-framework.md
- execution-cadence.md
- memory-template.md
- scale-diagnostic.md