SKILLEMALL.ai

DB ralphex-adopt

Convert plans from various source formats (OpenSpec, spec-kit, GitHub/GitLab issues with checklists, generic task-lists, free-form markdown) into ralphex-format plans in docs/plans/. Triggers on "ralphex-adopt", "adopt plan", "convert plan to ralphex", "import plan as ralphex".

umputun/ralphex Agent Skills author: umputun 1 file body ≈ 4 871 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Convert plans from various source formats (OpenSpec, spec-kit, GitHub/GitLab issues with checklists, generic task-lists, free-form markdown) into…

As a process B 79/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

GeneratorGitHubGitLabSoftware developmentAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
D
75/100
safety, quality, tests
Safety 60%
95
Quality 40%
45
Run on models
none yet
Process rating
B
79/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
70
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-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write Glob Grep AskUserQuestion

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

Against the Agent Skills spec

  • error name-missing SKILL.md: frontmatter has no `name`
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 79/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4871 tokens
  • 85Steps. 124 steps, 2 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (22 tags): a typed call is more reliable

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

  • +4Description does not say when NOT to use the skill (false activations)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 278: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 124 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 45.