SKILLEMALL.ai

BD report-builder

📥 openclaw skill install dabin0927/executive-briefing —— 给老板汇报不用写 PPT,一页纸让他 3 分钟拍板。 自动把几十页方案压缩成决策摘要:结论直接给、数据会说话、行动能落地。 BLUF 一页纸 + So What 叙事 + 脚本化工具链(init/bump/validate/density)。 适合:向 CEO/董事会/投资人汇报、决策备忘录、管理简报。 不适合:技术文档、数据分析图表、PPT 排版。 (EN) One-page executive briefing factory — BLUF + scripts + templates.

ClawHub Agent Skills author: DaBin0927 v2.1.3 MIT-0 23 files body ≈ 1 856 tokens Open the sourceclawhub.ai analyzed 2 d ago

📥 openclaw skill install dabin0927/executive-briefing —— 给老板汇报不用写 PPT,一页纸让他 3 分钟拍板。 自动把几十页方案压缩成决策摘要:结论直接给、数据会说话、行动能落地。 BLUF 一页纸 + So What 叙事 +…

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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: 0. 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")
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 41/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (report-builder) differs from the folder (executive-briefing)
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 100Steps. 15 steps
  • 100Execution cost. Instruction body is 1856 tokens
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 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

  • +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
  • -229 emoji in the instructions: noise for the model
  • -31 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 305: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

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

External checks

ClawHub: clean
This skill is a coherent report-building helper with local templates and scripts, though users should be aware that some triggers are broad and one workflow can initiate web research through another skill.
LLM: benign (high) · VirusTotal: · 3 Aug 2026