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AD memic-sdk

Context engineering platform for AI agents. Upload documents, search with semantic + structured queries, and inject relevant context into LLM prompts. Supports RAG, Text2SQL, hybrid search, metadata filters, and multi-tenant isolation. Use this skill to integrate Memic into any AI agent, copilot, or application that needs grounded context from documents and databases.

ClawHub Agent Skills author: Punith v1.0.4 3 files body ≈ 2 670 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (memic-sdk) differs from the folder (memic)
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 27 steps
    • 100Execution cost. Instruction body is 2670 tokens
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 370: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

    External checks

    ClawHub: clean
    This is a documentation-only Memic SDK skill whose sensitive data flows are mostly expected for a managed RAG and Text2SQL service.
    LLM: benign (high) · VirusTotal: · 29 May 2026