SKILLEMALL.ai

BD Pangolin SafeYield

Pangolin-SafeYield is a defensive-offensive investment AI agent. It runs a five-dimensional resonance scan (macro, fundamental, technical, capital flow, risk) across stocks and ETFs, then outputs buy/hold/sell advice. The macro framework covers 6 dimensions: US monetary policy & dollar liquidity, China monetary policy, exchange rate & cross-border flows, US-China economic cycle positioning, sector earnings, and real-time geopolitical risk assessment with dedicated US-Iran situation analysis (ceasefire status, Strait of Hormuz, oil price mapping, three-scenario modeling). Built on the Gecheng "Defensive-Offensive Investment System". Keywords: investment analysis, five-dimensional resonance, macro analysis, US Iran ceasefire, Strait of Hormuz, oil crisis, Fed, PBOC, liquidity, economic cycle, PE/PB valuation, Chan Theory MACD, Granville buy points, position allocation, portfolio management, 五面共振, 美伊谈判, 霍尔木兹海峡, 宏观分析, 美联储, 中国人民银行, 防御式进攻.

ClawHub Agent Skills author: lingfeng-19 v1.1.1 MIT-0 2 files body ≈ 2 465 tokens Open the sourceclawhub.ai analyzed 2 d ago

Pangolin-SafeYield is a defensive-offensive investment AI agent.

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsMarketingFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
59
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "capabilities"

Process rating: all ten parameters 41/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Pangolin SafeYield) differs from the folder (pangolin-safe-yield)
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 30 steps
  • 100Execution cost. Instruction body is 2465 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 947: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -271 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: clean
This is a text-only investment analysis workflow with no executable code or privileged system access, though its broad triggers and financial-advice framing deserve care.
LLM: benign (high) · VirusTotal: · 2 Jun 2026