SKILLEMALL.ai
GitHub ↗ RU Sign in

AB ragora

Use Ragora MCP tools and REST API to discover, search, and synthesize answers from knowledge bases. Trigger when the user asks for grounded answers from Ragora collections, cross-collection comparison, source-backed summaries, due diligence research, or verification using marketplace data.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 7 718 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use Ragora MCP tools and REST API to discover, search, and synthesize answers from knowledge bases.

As a process B 74/100 · Nearly there — weak spots: inputs and preconditions

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
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
77
Run on models
none yet
Process rating
B
74/100
Nearly there
Inputs and preconditions w 11
30
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security SKILL.md:576
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    1. `search("Company X security audit penetration test vulnerability", top_k=15)` — broad discovery pass.
    quoted

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

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7718 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 74/100

  • 30Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 7718 tokens
  • 100Steps. 139 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (12 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 290: enough signal without eating the budget
  • +4Structure: 76 headings
  • +3Step-by-step instructions: 139 items
  • +3Output format is stated explicitly
  • +4Has examples (46 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.