SKILLEMALL.ai

BB stock-sentiment

Sentiment and smart-money positioning for US stocks.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Senti v0.4.4 MIT-0 3 files body ≈ 7 375 tokens Open the sourceclawhub.ai analyzed 2 d ago

Sentiment and smart-money positioning for US stocks.

As a process B 66/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
76/100
safety, quality, tests
Safety 60%
76
Quality 40%
75
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 3

  • high Concealment en-hide-from-user SKILL.md:265
    Instruction to hide actions from the user
    Synthesize the setup as educational context: "$AAPL earnings in 5d: sentiment +0.22 over 30d and trending up; insiders net sellers (2 sells, 0 buys); EPS consensus $1.52 (range $1.48 to $1.55, 28 anal
Medium and low: 2
  • medium Exfiltration net-credential-use SKILL.md:73
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "X-SentiSense-API-Key: $SENTISENSE_API_KEY" \
  • low Exfiltration net-credential-use SKILL.md:296
    Credential used in a network call (verify the destination is the intended service) (security demo / example; quoted — discussed, not commanded)
    1. **Reachability and auth.** Every endpoint here takes an API key, so one call checks both: `curl -s -o /dev/null -w "%{http_code}" -H "X-SentiSense-API-Key: $SENTISENSE_API_KEY" "https://app.sentise
    demoquoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7375 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "requires"
  • note frontmatter-key unknown frontmatter key "primaryEnv"

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 6 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 7375 tokens
  • 100Steps. 51 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 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
  • +3Description length 52: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill is a coherent read-only stock sentiment data integration that uses a provider API key and does not show hidden persistence, local data access, or trading authority.
LLM: benign (high) · VirusTotal: · 8 Sept 2026