SKILLEMALL.ai
GitHub ↗ RU Sign in

AC x-alive

Bring your AI agent to life on X/Twitter. Complete toolkit for launching, growing, and maintaining an authentic AI presence — organic replies, trend awareness, dedup, and safety. Use when setting up a new agent on X, defining voice/personality, creating content strategy, automating posts, managing engagement, handling safety (scams, impersonation, tokens), or growing a following organically.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 3 535 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Bring your AI agent to life on X/Twitter.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorGitHubAI and agentstype 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
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
80
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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 Exfiltration net-credential-use SKILL.md:23
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      3. **Your X user ID** — fetch it: `curl -s "https://api.x.com/2/users/by/username/YOUR_HANDLE" -H "Authorization: Bearer $X_BEARER_TOKEN"` — save this for dedup checks
      quoted

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 143 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3535 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 19 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 394: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 143 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.