SKILLEMALL.ai

BC vendor-summit-report

Build an executive-oriented deep-dive report for a major data/AI vendor conference — Snowflake Summit, Databricks Data+AI Summit, Microsoft Build, Google Cloud Next, AWS re:Invent. Ships three aligned deliverables (light-theme HTML, full Markdown, enterprise-chat push version) across a fixed 8-section structure held to analyst-note standards — state-don't-instruct, vendor-data caveats, role-based buyer recommendations, and confidence-tagged planning assumptions. Use it when the user asks for a 深度专题报告 / 大会报告 / summit report on a named event, or wants a previous summit report's structure repeated for a new conference.

ClawHub Agent Skills author: haiyangchen v1.0.0 MIT-0 8 files body ≈ 7 000 tokens Open the sourceclawhub.ai analyzed 2 d ago

Build an executive-oriented deep-dive report for a major data/AI vendor conference — Snowflake Summit, Databricks Data+AI Summit, Microsoft Build, Google…

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, running it twice

GeneratorAWSGoogle CloudResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Failures and branches w 10
50
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 ≈ 7000 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "read_when"
  • note frontmatter-key unknown frontmatter key "not_for"

Process rating: all ten parameters 63/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 16 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 7000 tokens
  • 100Steps. 146 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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)
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 623: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 146 items
  • +3Output format is stated explicitly
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This skill is a coherent report-generation workflow with disclosed web research, local file output, templates, and a confirmation gate before any live publishing.
LLM: benign (high) · VirusTotal: · 11 Sept 2026