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AD deep-research

This skill should be used when the user requests comprehensive research, deep investigation, or detailed academic-style reports on any topic. Trigger phrases include "deep research", "comprehensive investigation", "detailed report", "academic research", or requests for thorough analysis of complex subjects. The skill produces multi-thousand word reports in markdown format with extensive citations.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 4 269 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill should be used when the user requests comprehensive research, deep investigation, or detailed academic-style reports on any topic.

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerData and analyticsResearchtype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (deep-research) differs from the folder (deep-research-2)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Steps. 130 steps, 7 vague phrases
    • 60Failures and branches. 2 branches
    • 70Execution cost. Instruction body is 4269 tokens
    • 100When it triggers. States when to use and when not to
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 400: enough signal without eating the budget
    • +4Structure: 49 headings
    • +3Step-by-step instructions: 130 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.