SKILLEMALL.ai
GitHub ↗ RU Sign in

AD xiaohongshu-reply-assistant

小红书智能回复助手 - 自动分析评论情感和意图，生成个性化回复建议。 Use for: (1) Analyzing Xiaohongshu comment sentiment and intent, (2) Generating personalized reply suggestions, (3) Managing comment history and batch processing, (4) Monitoring notes for new comments. Supports both standalone offline analysis and API integration with real Xiaohongshu data.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 322 tokens Open the sourcegithub.com↗ analyzed 29 h ago

小红书智能回复助手 - 自动分析评论情感和意图，生成个性化回复建议。 Use for: (1) Analyzing Xiaohongshu comment sentiment and intent, (2) Generating personalized reply suggestions, (3)…

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

ProcedureInfrastructureSoftware developmentData 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
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

The same skill appears in 1 more place: skill-library-mcp

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"

    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 (xiaohongshu-reply-assistant) differs from the folder (clawhub-publish)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 26 steps
    • 100Execution cost. Instruction body is 1322 tokens
    • low 12 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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 328: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.