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BB x-twitter

Personal X (Twitter) analytics — timeline engagement, mentions, follower tracking, read any tweet/thread, bookmarks, and combined briefings via X API v2. Cost-optimized with persistent local store and daily budget guards.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 2 122 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Personal X (Twitter) analytics — timeline engagement, mentions, follower tracking, read any tweet/thread, bookmarks, and combined briefings via X API v2.

As a process B 66/100 · Nearly there — weak spots: result and completion, consistency, running it twice

IntegrationData and analyticstype 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
B
85/100
safety, quality, tests
Safety 60%
98
Quality 40%
65
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Consistency w 8
40
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.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Dangerous commands cmd-pipe-to-shell-known-host SETUP.md:9
    Pipe-to-shell installer from a well-known host (still executes remote code) (documentation of a security skill)
    curl -LsSf https://astral.sh/uv/install.sh | sh
    security skill

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 14. 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")

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (x-twitter) differs from the folder (x-smart-read)
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 34 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 4 branches, has a failure section
  • 100Execution cost. Instruction body is 2122 tokens
  • 100Progress reporting. Reports progress

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 221: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.