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CF almanac-creator

Daily Chinese Almanac Image Generator - Create traditional Huangli calendar images with zodiac fortune, fengshui tips, auspicious timing, and dressing advice for social media publishing (Toutiao/Douyin/Xiaohongshu)

ClawHub Agent Skills author: zhangj85 v3.0.5 MIT-0 9 files · 2 scripts body ≈ 2 096 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Daily Chinese Almanac Image Generator - Create traditional Huangli calendar images with zodiac fortune, fengshui tips, auspicious timing, and dressing advice…

As a process F 33/100 · No process to follow — References files that are not bundled: references/almanac-data-template.md, references/publish-guide.md

GeneratorSoftware developmentMarketingPersonal productivitytype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
99
Quality 40%
61
Run on models
none yet
Process rating
F
33/100
No process to follow
References files that are not bundled: references/almanac-data-template.md, references/publish-guide.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-eval-dynamic references/usage-examples.md:56
    Dynamic code execution from decoded/untrusted input (test fixture / example file)
    os.system(f'python skills/almanac-creator/scripts/generate_almanac.py --date {date_str}')
    fixture

Files scanned: 9. 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")
  • warning missing-ref reference to a missing file: references/almanac-data-template.md
  • warning missing-ref reference to a missing file: references/publish-guide.md
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/almanac-data-template.md, references/publish-guide.md
  • 0Tools and files. 2 referenced file(s) missing: references/almanac-data-template.md, references/publish-guide.md
  • 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. 2 mutating operations with no state check
  • 100Steps. 82 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2096 tokens
  • low 11 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
  • -2142 emoji in the instructions: noise for the model
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 214: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 82 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This skill is a disclosed Chinese almanac image generator with mostly local file output; users should review its built-in social engagement footer and optional third-party API examples before publishing.
LLM: benign (high) · VirusTotal: · 16 Sept 2026