SKILLEMALL.ai

BF consensus-engineer

AI solution architect for consensus-tools. Interactive multi-step skill that analyzes your project, recommends consensus-tools integration, scaffolds setup, and proves it works with auditability. Covers guard evaluation, consensus voting, persona management, workflow orchestration, and MCP tool integration.

ClawHub Agent Skills author: Kai Cianflone v1.0.0 MIT-0 5 files body ≈ 3 860 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 47/100 · Will not run — References files that are not bundled: examples/wrapper-demo

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
95
Quality 40%
63
Run on models
none yet
Process rating
F
47/100
Will not run
References files that are not bundled: examples/wrapper-demo
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope 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 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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Grep Glob Bash AskUserQuestion Agent

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: examples/wrapper-demo
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "upstream"

Process rating: all ten parameters 47/100

Will not run. References files that are not bundled: examples/wrapper-demo
  • 0Tools and files. 1 referenced file(s) missing: examples/wrapper-demo
  • 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
  • 100Steps. 83 steps
  • 100Failures and branches. 8 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3860 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 308: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 83 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This is a disclosed, interactive setup assistant for consensus-tools that may modify a project, but its requested access fits its stated purpose.
LLM: benign (high) · VirusTotal: · 29 May 2026