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BD twitter-agent-skill

Cookie-based Twitter/X automation toolkit (timeline, notifications, posting, follow ops) for OpenClaw agents.

modbender/skill-library-mcp Agent Skills author: modbender MIT 33 files body ≈ 295 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Cookie-based Twitter/X automation toolkit (timeline, notifications, posting, follow ops) for OpenClaw agents.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
72
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Exfiltration read-dotenv README.md:37
    Reads a .env file
    copy .env.example .env  # fill in your auth_token + ct0 values
  • low Secrets in code secret-labelled-token twitter_api/demo_langchain_tools.py:32
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)
    AUTH_TOKEN = "f06e…318"
    placeholder
  • low Secrets in code secret-high-entropy-token twitter_api/utils/constants.py:9
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    'authorization': 'Bearer AAAA…uTs%3D1Z…TnA',
    quoted

Files scanned: 33. 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 39/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (twitter-agent-skill) differs from the folder (twitter-api)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 85Steps. 10 steps, 3 vague phrases
  • 100Execution cost. Instruction body is 295 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Description length 109: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 4 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (1 code blocks)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.