SKILLEMALL.ai

BC stanley-druckenmiller-workflow

Thesis-driven macro-to-execution market workflow in natural Chinese or English. Generate A-share and U.S. equity Morning Briefs, Intraday Alerts, Close Reviews, Weekly Regime Resets, and pre-trade sanity checks. Use when the user asks for an A-share morning brief, a U.S. morning brief, a pre-market view, an intraday state update, an end-of-day review, a weekly regime reset, a market-location read, portfolio-bias guidance, falsification conditions, market priority, industry priority, or a translation from liquidity, rates, credit, real-economy demand, price, structure, sector expression, fundamentals, and reflexivity into Regime, Best Expression, Position Bias, Kill-switch, and Watchlist.

ClawHub Agent Skills author: luckycatL v1.1.11 MIT-0 7 files body ≈ 9 649 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost

ProcedureInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9649 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Execution cost. Instruction body is 9649 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 605 steps
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 18 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill

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)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 696: enough signal without eating the budget
  • +4Structure: 91 headings
  • +3Step-by-step instructions: 605 items
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This is a coherent public-market analysis skill that fetches financial data and caches snapshots locally, with the sensitive behavior disclosed and aligned to its purpose.
LLM: benign (high) · VirusTotal: benign · 28 May 2026