SKILLEMALL.ai

AB undertow

Skill discovery engine for AI coding agents. Recommends and installs the right skill when you need it — code review, test generation, debugging, commit messages, PR preparation, security scanning, dependency audits, Docker setup, CI/CD pipelines, API documentation, refactoring, performance optimization, bundle analysis, git recovery, README generation, license compliance, migration guides, dead code removal, and secret detection. One install gives your agent access to a curated library of 20+ developer workflow skills. Use when the user asks for help with any development workflow, code quality, DevOps, security, testing, documentation, or project setup task.

ClawHub Hermes author: 8co v0.2.2 MIT-0 3 files body ≈ 2 592 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 73/100 · Nearly there — weak spots: result and completion, inputs and preconditions

IntegrationDockerSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
30
Result and completion w 14
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 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 description-long-hermes description is 666 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 73/100

  • 30Inputs and preconditions. Does not say what the process needs to start
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 43 steps
  • 100Failures and branches. 10 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2592 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 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 666: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
Undertow is a disclosed skill-discovery helper that can recommend and install other skills only after user confirmation, with privacy and scope caveats.
LLM: benign (high) · VirusTotal: · 29 May 2026