SKILLEMALL.ai
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AF deso-research

Research and analyze content across decentralized social networks (Farcaster, Lens, Nostr, Bluesky) using the deso-ag CLI tool. Use this skill when users want to research topics on decentralized social platforms, analyze trending content, extract discussion terms, browse Farcaster channels, or compare engagement across networks. Trigger on phrases like "research X on Farcaster", "what's trending on Lens", "analyze [topic] across deso networks", "search deso for [topic]", "extract trending terms", "browse Farcaster channels", "what are people saying about X on Farcaster/Lens/Nostr/Bluesky", or any query about decentralized social media content. Make sure to use this skill for any decentralized social research tasks, even if the user just says "check Farcaster" or "look up [topic] on Lens".

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 936 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Research and analyze content across decentralized social networks (Farcaster, Lens, Nostr, Bluesky) using the deso-ag CLI tool.

As a process F 68/100 · Will not run — References files that are not bundled: references/command-reference.md

AnalyzerMarketingAI and agentsWriting and documentstype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
F
68/100
Will not run
References files that are not bundled: references/command-reference.md
Tools and files w 18
0
Progress reporting w 2
0
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

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

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/command-reference.md

Process rating: all ten parameters 68/100

Will not run. References files that are not bundled: references/command-reference.md
  • 0Tools and files. 1 referenced file(s) missing: references/command-reference.md
  • 0Progress reporting. Says nothing while it works
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 9 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1936 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

  • +4Description does not say when NOT to use the skill (false activations)
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +3Description length 799: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 9 items
  • +3Output format is stated explicitly
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.