SKILLEMALL.ai
GitHub ↗ RU Sign in

BD tibo-reset-codex

查询 ChatGPT/Codex 重置公告及多个 Pro 账号的剩余额度、备用 Full reset；复用 Google 登录逐个核实并恢复网页账号，换算北京时间。区分 Tibo 官宣、未官宣的平台静默重置、banked reset 与账户级周期重置。Use when 用户问「什么时候重置」「额度什么时候恢复」「下次全员重置几点」 「banked reset 到了吗」「Tibo 说了什么」「usage limit when reset」，或说「额度突然回到 100%」「好像/肯定又重置了」，或问「几个账号都用完了吗」「哪个还满额」。必须用实时产品状态、独立用户实测与公告交叉核验；禁止因 Tibo Radar 没有新条目就否定已经发生的重置，并须把太平洋时间当场换算为北京时间。

daymade/claude-code-skills Agent Skills author: daymade MIT 31 files · 23 scripts body ≈ 10 673 tokens Open the sourcegithub.com↗ analyzed 33 h ago

查询 ChatGPT/Codex 重置公告及多个 Pro 账号的剩余额度、备用 Full reset；复用 Google 登录逐个核实并恢复网页账号，换算北京时间。区分 Tibo 官宣、未官宣的平台静默重置、banked reset 与账户级周期重置。Use when…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePlaywrightSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 16. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 10673 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 45/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 40Execution cost. Instruction body is 10673 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Steps. 99 steps
  • 100Consistency. Name and required fields are in place
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 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
  • -31 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 342: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 99 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.