技能详情（站内镜像，无评论）
作者：danyangliu @danyangliu-sandwichlab
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.1
统计：⭐ 0 · 166 · 1 current installs · 1 all-time installs
⭐ 0
安装量（当前） 1
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：danyangliu-sandwichlab/growth-strategy-hub
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
The skill is an instruction-only decision framework whose stated purpose, inputs, and outputs are internally consistent and do not request extraneous credentials or installs.
目的
Name/description match the SKILL.md content: an enterprise-level capital allocation decision system. Required inputs (enterprise targets, capital pool, market signals, governance rules) are appropriate and proportional to the stated purpose.
说明范围
SKILL.md contains workflow, input/output contracts, decision rules, examples, and guardrails but does not instruct the agent to fetch platform data, read unrelated files, or access credentials. It assumes market_signal_inputs are supplied; it is ambiguous about who/what supplies those signals (user, data pipeline, or agent). If the agent is later given credentials or external fetch capability, actual behavior will depend on how it's integrated.
安装机制
No install spec and no code files — instruction-only. Nothing will be written to disk or installed by the skill itself.
证书
No required environment variables, no credentials, and no config paths are declared. The inputs are data-oriented rather than credential-oriented, which is proportionate for a decision system that consumes supplied signals.
持久
always is false and the skill does not request persistent presence or modification of other skills or system settings. Normal autonomous invocation is allowed (platform default).
综合结论
This skill is an instruction-only decision framework and appears coherent with its stated purpose. Important considerations before installing or enabling it for autonomous use: (1) it expects market_signal_inputs but does not fetch platform data itself — if you plan to grant the agent credentials or connectors to Google/Meta/TikTok, treat those credentials as sensitive and limit scope (read-only, scoped access) and require human approval for e…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Growth Strategy Hub」。简介：Enterprise Growth Decision System built on real market signals and governing ca…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/danyangliu-sandwichlab/growth-strategy-hub/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: growth-strategy-hub
description: Enterprise Growth Decision System built on real market signals and governing capital allocation across Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, and DSP/programmatic.
---
# Growth Strategy Hub
## Purpose
Core mission:
- Operate an Enterprise Growth Decision System.
- Built on real market signals.
- Govern capital allocation with explicit risk controls.
- Translate enterprise goals into decision-ready investment policies.
## When To Trigger
Use this skill when the user asks for:
- enterprise-level growth investment decisions
- capital allocation governance across channels and markets
- quarterly or annual growth portfolio strategy
- risk warning and executive decision support
High-signal keywords:
- enterprise growth, decision system, capital allocation
- revenue, profit, cashflow, roi, roas, ltv
- forecast, model, strategy, risk, budget
## Input Contract
Required:
- enterprise_targets: revenue/profit/cashflow targets
- capital_pool: deployable budget and restrictions
- market_signal_inputs: cost, demand, competition, conversion signals
- governance_rules: risk limits and approval thresholds
Optional:
- scenario_definitions
- balance_sheet_constraints
- board_preferences
- downside_tolerance
## Output Contract
1. Market-Signal Decision Brief
2. Capital Allocation Portfolio Plan
3. Scenario Forecast (base/upside/downside)
4. Risk Warning System and thresholds
5. Executive Decision Memo
## Workflow
1. Normalize enterprise targets and capital constraints.
2. Ingest and weight real market signals.
3. Simulate allocation outcomes by scenario.
4. Evaluate risk and trigger conditions.
5. Output governed capital allocation decisions.
## Decision Rules
- If market signal confidence is low, reduce capital concentration.
- If downside risk exceeds governance limit, switch to defensive allocation mode.
- If upside scenario is strong but volatility is high, stage releases by milestone.
- If cashflow protection conflicts with growth speed, prioritize solvency guardrails.
## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic
Platform behavior guidance:
- Use platform outputs as market signals, not isolated KPIs.
- Keep final decisions at portfolio allocation level.
## Constraints And Guardrails
- Never present speculative outputs as deterministic outcomes.
- Keep capital governance explicit and auditable.
- Separate strategic recommendation from execution details.
## Failure Handling And Escalation
- If signal quality is inconsistent, provide confidence bands and data remediation needs.
- If governance rules are missing, apply strict default risk policy.
- If decision is high-stakes with low confidence, require executive review gate.
## Code Examples
### Capital Allocation Scenario (YAML)
decision_system: growth_strategy_hub
horizon: Q4-2026
capital_pool: 1800000
allocations:
Meta: 0.28
GoogleAds: 0.26
TikTokAds: 0.14
AmazonAds: 0.12
DSP: 0.20
risk_mode: balanced
### Risk Warning Rule (JSON)
{
"trigger": "blended_roas_below_floor",
"floor": 2.1,
"window_days": 7,
"action": "freeze_incremental_capital"
}
## Examples
### Example 1: Capital portfolio reset
Input:
- Need to rebalance channel spend with stricter risk policy
Output focus:
- new allocation portfolio
- scenario impacts
- governance gates
### Example 2: High-growth vs cashflow conflict
Input:
- Aggressive growth target with limited cash buffer
Output focus:
- trade-off framework
- staged capital release
- risk warning thresholds
### Example 3: Board decision support
Input:
- Quarterly enterprise growth review
Output focus:
- decision memo
- signal-backed recommendations
- contingency plan
## Quality Checklist
- [ ] Required sections are complete and non-empty
- [ ] Trigger keywords include at least 3 registry terms
- [ ] Input and output contracts are operationally testable
- [ ] Workflow and decision rules are capability-specific
- [ ] Platform references are explicit and concrete
- [ ] At least 3 practical examples are included