SKILLEMALL.ai
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AF Intelligence Ingestion

Analyze and evaluate URLs, links, articles, tweets, and external info sources for strategic value. NOT a summarizer — this skill classifies, scores importance, maps to architecture, stores structured notes in Obsidian, updates the Strategic Landscape, and auto-generates draft Skills when new capabilities are detected (Auto-Skill Synthesis). Use when: user shares a link (x.com, github.com, arxiv.org, any URL), pastes article text, says "analyze this", "evaluate this", "what do you think about this", or forwards content. Do NOT use for: simple summarization requests (use summarize skill instead).

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 3 401 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze and evaluate URLs, links, articles, tweets, and external info sources for strategic value.

As a process F 52/100 · Will not run — References files that are not bundled: URL

AnalyzerObsidianGitHubWriting 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
F
52/100
Will not run
References files that are not bundled: URL
Tools and files w 18
0
Running it twice w 4
30
Consistency w 8
40
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning missing-ref reference to a missing file: URL

Process rating: all ten parameters 52/100

Will not run. References files that are not bundled: URL
  • 0Tools and files. 1 referenced file(s) missing: URL
  • 30Running it twice. 12 mutating operations with no state check
  • 40Consistency. Frontmatter name (Intelligence Ingestion) differs from the folder (intelligence-ingestion)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 32 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 3401 tokens
  • 100Progress reporting. Reports progress
  • low 11 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

  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 601: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 32 items
  • +3Output format is stated explicitly
  • +4Has examples (13 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.