SKILLEMALL.ai

AC riskofficer

Portfolio risk management and analytics. Use when user asks to calculate VaR, run Monte Carlo, stress test, optimize with Risk Parity / Calmar / Black-Litterman, run pre-trade check, check sector concentration, manage portfolios, or analyze cross-portfolio correlation. Also covers ticker search, broker sync, batch portfolio creation, and portfolio comparison.

ClawHub Agent Skills author: mib424242 v4.3.0 17 files body ≈ 11 887 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureData and analyticsFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11887 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 11887 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 160 steps
  • 100Failures and branches. 6 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • +1No license
  • +3Description length 361: enough signal without eating the budget
  • +4Structure: 74 headings
  • +3Step-by-step instructions: 160 items
  • +4Has examples (43 code blocks)
  • +4Reference files are cited in the instructions (13 of 13)
  • +2Bilingual instructions (RU + EN)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This is a disclosed RiskOfficer integration for portfolio analytics and virtual portfolio management, with sensitive account-token use and destructive API actions that users should handle carefully.
LLM: benign (medium) · VirusTotal: benign · 10 Sept 2026