BD zeelin-twitter-x-auto-ops
Automate Twitter/X growth and content operations. Discover AI trends, generate tweets, auto post to X, find follow‑back threads, comment for engagement, quote viral tweets, and help grow followers. Trigger when users ask to post tweets, run Twitter/X automation, generate AI tweets, grow followers, comment on mutual follow threads, manage Tech Twitter, or operate a Twitter/X account. Keywords: twitter, x automation, tweet generator, AI tweet, tech twitter, twitter growth, follow back, quote tweet, 发推, 推特运营, X运营, 自动发推, 涨粉, 互关, AI推文.
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 14. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Automate Twitter/X growth and content operations. Discover AI tren… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 9 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 7 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1672 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- -37 of 12 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 536: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 7 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.