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AB crif

Crypto Research Interactive Framework — interactive crypto deep-research with human-AI collaboration. Use this skill when users want to research crypto projects, analyze sectors or markets, compare protocols, evaluate tokenomics/teams/products, track traction metrics, assess technology architecture, create content from research, generate AI image prompts, review research quality, brainstorm crypto ideas, or plan multi-workflow research. Trigger on any mention of: crypto analysis, project evaluation, sector overview, competitive analysis, DeFi/NFT/L1/L2 research, token analysis, market intelligence, investment thesis, research brief, content creation from crypto research, or any crypto/blockchain research needs.

ClawHub Agent Skills author: kudodefi v0.1.1 MIT-0 80 files body ≈ 1 621 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, progress reporting

ProcedureAI and agentsData and analyticstype 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SECURITY.md:84
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **No credential harvesting** — Does not collect API keys, passwords, or credentials

    Files scanned: 80. 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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 33 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1621 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +2Single-language instructions
    • +3Description length 720: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 33 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a prompt-only crypto research framework whose file and network behavior is mostly disclosed and aligned with producing local research reports.
    LLM: benign (high) · VirusTotal: benign · 28 May 2026