SKILLEMALL.ai

BD Multi-Agent Dev Team

2-agent collaborative software development workflow for OpenClaw

ClawHub Agent Skills author: Chloe Park v1.0.0 8 files body ≈ 2 019 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
95
Quality 40%
61
Run on models
none yet
Process rating
D
48/100
Unfinished process
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

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.
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-agent-memory-dump agents/dev-agent/SOUL.md
    Agent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokens
    agents/dev-agent/SOUL.md, agents/pm-agent/SOUL.md

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 48/100

  • 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
  • 30Running it twice. 8 mutating operations with no state check
  • 40Consistency. Frontmatter name (Multi-Agent Dev Team) differs from the folder (multi-agent-dev-team)
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 85Steps. 62 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 2019 tokens
  • 100Progress reporting. Reports progress
  • low 13 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 64: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -219 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This is a disclosed software-development automation skill whose file, command, Git, and agent-coordination powers match its stated purpose, though users should run it only in a dedicated project workspace.
LLM: benign (medium) · VirusTotal: suspicious · 28 May 2026