SKILLEMALL.ai
GitHub ↗ RU Sign in

BC social-post

Post and reply to X/Twitter and Farcaster with text and images. Features multi-account support, auto-variation to avoid duplicate content detection, draft preview, character validation, threads, replies, and image uploads. Consumption-based pricing for X API, pay-per-cast for Farcaster.

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files · 9 scripts body ≈ 3 133 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Post and reply to X/Twitter and Farcaster with text and images.

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationAI 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
79/100
safety, quality, tests
Safety 60%
85
Quality 40%
70
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Risky intent medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

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 · 7

✓ No critical or high findings

Medium and low: 7
  • medium Risky intent intent-wallet-secrets README.md:127
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - Export your custody wallet private key
  • medium Risky intent intent-wallet-secrets SKILL.md:150
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - Export your custody wallet private key
  • low Exfiltration read-dotenv lib/twitter.sh:8
    Reads a .env file
    source /home/phan_harry/.openclaw/.env
  • low Secrets in code secret-high-entropy-token README.md:159
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Payment to: `0xA6…1A1`
    quoted
  • low Secrets in code secret-high-entropy-token scripts/check-balance.sh:7
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    USDC_BASE="0x83…913"
    quoted
  • low Exfiltration read-dotenv scripts/reply.sh:272
    Reads a .env file
    source /home/phan_harry/.openclaw/.env
  • low Secrets in code secret-high-entropy-token SKILL.md:385
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Sent to Neynar Hub: `0xA6…1A1`
    quoted

Files scanned: 12. 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 50/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Steps. 88 steps, 5 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3133 tokens
  • 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -216 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 287: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 88 items
  • +4Has examples (18 code blocks)
  • +3All 3 scripts are documented
  • +1License stated

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