SKILLEMALL.ai

BC insight-claw-hermes

Download, configure, run, verify, and troubleshoot Insight Claw, an A-share self-selected stock analysis pipeline.

Not recommendedcritical or high security findings
ClawHub Hermes author: GeraltJc v0.3.1 MIT-0 4 files body ≈ 2 973 tokens Open the sourceclawhub.ai analyzed 2 d ago

Download, configure, run, verify, and troubleshoot Insight Claw, an A-share self-selected stock analysis pipeline.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
80
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
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

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.

Broad scope
If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: AIHUBMIX_KEY, OPENAI_API_KEY (high-value: OPENAI_API_KEY) — verify each one is needed for the stated purpose
    required_environment_variables: AIHUBMIX_KEY, OPENAI_API_KEY
Medium and low: 2
  • low Exfiltration read-dotenv references/quickstart.md:39
    Reads a .env file
    [ -f .env ] || cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:133
    Reads a .env file (quoted — discussed, not commanded)
    6. Before the first real analysis run, verify that a usable LLM credential is available through Hermes secret handling, the user's shell environment, or a user-approved local `.env` merge. If `.env` i
    quoted

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 114 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "required_environment_variables"

Process rating: all ten parameters 53/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
  • 60Consistency. The Hermes dialect needs category and tags
  • 100Steps. 45 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 2973 tokens
  • 100Running it twice. Mutating operations check current state
  • 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)
  • +3Description length 114: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 45 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
The artifact is a coherent set of repo-maintainer and Convex workflow skills with disclosed high-impact operations and explicit user/operator gates.
LLM: benign (medium) · VirusTotal: · 14 Jun 2026