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AC social-media-ops

Set up a complete multi-brand social media management team on OpenClaw. Scaffolds 7 specialized AI agents (Leader, Researcher, Content Strategist, Visual Designer, Operator, Engineer, Reviewer) in a star topology with persistent A2A sessions, 3-layer memory system, shared knowledge base, approval workflows, and brand isolation. Use when setting up a new social media operations team, adding the multi-agent framework to an existing OpenClaw instance, or when the user mentions social media management, multi-brand operations, or content team setup.

modbender/skill-library-mcp Agent Skills author: modbender MIT 43 files · 1 script body ≈ 3 626 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Set up a complete multi-brand social media management team on OpenClaw.

As a process C 63/100 · Has gaps — weak spots: result and completion, progress reporting

ProcedureTelegramMarketingtype 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
93/100
safety, quality, tests
Safety 60%
95
Quality 40%
91
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
55
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
    • medium Broad scope meta-agent-memory-dump assets/workspace/HEARTBEAT.md
      Agent memory / workspace files bundled with the skill (9) — likely a workspace dump with personal data or tokens
      assets/workspace/HEARTBEAT.md, assets/workspace/IDENTITY.md, assets/workspace/SOUL.md, assets/workspace-content/SOUL.md, assets/workspace-designer/SOUL.md

    Files scanned: 43. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 110 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3626 tokens
    • 100Running it twice. Mutating operations check current state
    • low 10 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 550: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 110 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.