SKILLEMALL.ai

AC create-technology-insight-report-rd

Create or rigorously review a source-traceable HTML technology-insight report that integrates patents, scientific literature, market and company evidence, standards, regulation, engineering evidence, technology routes, competitive context, candidate evidence gaps, emerging applications, claim-relevance screening, technical options, and decision actions. Use for a full technology-domain insight report or for auditing and localizing an existing report package.

ClawHub Agent Skills author: yuanzhian-patsnap v1.0.0 MIT-0 8 files body ≈ 8 542 tokens Open the sourceclawhub.ai analyzed 3 d ago

Create or rigorously review a source-traceable HTML technology-insight report that integrates patents, scientific literature, market and company evidence…

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Execution cost w 6
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8542 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "copyright"

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Execution cost. Instruction body is 8542 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 85Steps. 392 steps, 1 vague phrases
  • 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
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 41 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 462: enough signal without eating the budget
  • +4Structure: 113 headings
  • +3Step-by-step instructions: 392 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This skill is a report-building workflow with proportionate local QA scripts and clearly disclosed research, evidence, and legal-boundary safeguards.
LLM: benign (high) · VirusTotal: · 13 Aug 2026