SKILLEMALL.ai

AA results-summary-report-generator

Evaluate and revise an academic Results section using six evidence-based dimensions, then produce a prioritized results-quality summary report with scores, concrete problems, evidence, and actionable revisions while preserving the Results/Discussion boundary.

ClawHub Agent Skills author: linuoxu v1.0.0 MIT-0 27 files body ≈ 1 663 tokens Open the sourceclawhub.ai analyzed 2 d ago

Evaluate and revise an academic Results section using six evidence-based dimensions, then produce a prioritized results-quality summary report with scores…

As a process A 81/100 · Runs to the end — weak spots: progress reporting

AnalyzerData and analyticsWriting and documentsResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Failures and branches w 10
55
Result and completion w 14
60
the three weakest of ten parameters · all ten

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 · 0

✓ No critical or high findings

Files scanned: 13. 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")

Process rating: all ten parameters 81/100

  • 0Progress reporting. Says nothing while it works
  • 55Failures and branches. 1 branches
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 37 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1663 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 259: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 37 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is a purpose-aligned academic writing evaluation skill with no executable code, persistence, credential use, or hidden high-impact behavior.
LLM: benign (high) · VirusTotal: · 27 Aug 2026