SKILLEMALL.ai

AF hedgefundmonitor-hardened

Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. No API key or registration required. Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data.

ClawHub Agent Skills author: Faberlens v1.0.0 MIT-0 3 files body ≈ 1 836 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 36/100 · Will not run — References files that are not bundled: references/api-overview.md, references/endpoints-metadata.md, references/endpoints-series-data.md

IntegrationData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: references/api-overview.md, references/endpoints-metadata.md, references/endpoints-series-data.md
Tools and files w 18
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. The text references files that are not there: add them or drop the references.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/api-overview.md
  • warning missing-ref reference to a missing file: references/endpoints-metadata.md
  • warning missing-ref reference to a missing file: references/endpoints-series-data.md
  • warning missing-ref reference to a missing file: references/endpoints-combined.md
  • warning missing-ref reference to a missing file: references/datasets.md
  • warning missing-ref reference to a missing file: references/parameters.md
  • warning missing-ref reference to a missing file: references/examples.md

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: references/api-overview.md, references/endpoints-metadata.md, references/endpoints-series-data.md
  • 0Tools and files. 7 referenced file(s) missing: references/api-overview.md, references/endpoints-metadata.md, references/endpoints-series-data.md
  • 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
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (hedgefundmonitor-hardened) differs from the folder (bloomberg-api-hardened)
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 20 steps
  • 100Execution cost. Instruction body is 1836 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +2Single-language instructions
  • +3Description length 618: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 20 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This appears to be a public OFR hedge fund data helper, but its package identity, capability tags, and safety documentation are inconsistent enough that users should review it before installing.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026