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BD work-report

工作汇报生成器（日报→周报→月报→年报，层层叠加）。 用户只需提供几个关键词或口水话，自动生成自然的工作汇报。 强制去AI味（白话表达、口语断句、禁止作文结构）。 触发词：日报、周报、月报、年报、工作汇报、工作总结，"今天搞了"、"这周做了"等任意输入。

ClawHub Hermes author: jiuwu2495 v1.0.0 MIT-0 2 files body ≈ 1 238 tokens Open the sourceclawhub.ai↗ analyzed 31 h ago

工作汇报生成器（日报→周报→月报→年报，层层叠加）。 用户只需提供几个关键词或口水话，自动生成自然的工作汇报。 强制去AI味（白话表达、口语断句、禁止作文结构）。 触发词：日报、周报、月报、年报、工作汇报、工作总结，"今天搞了"、"这周做了"等任意输入。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePersonal productivityAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
D
46/100
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 128 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"

Process rating: all ten parameters 46/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (work-report) differs from the folder (work-report-pro)
  • 100Tools and files. No external tools needed
  • 100Steps. 40 steps
  • 100Execution cost. Instruction body is 1238 tokens

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
  • -215 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 127: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (13 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
This skill is a coherent work-report generator that reads and saves reports in a disclosed workspace folder, with no evidence of malware, exfiltration, or hidden unrelated behavior.
LLM: benign (high) · VirusTotal: · 5 Jun 2026