BC social-media-agent
Autonomous social media management for X/Twitter using only OpenClaw native tools. Use when a user wants to automate X posting, generate content, track engagement, or build an audience. Triggers on requests about tweets, social media strategy, X engagement, content calendars, or growing a following. No API keys required — uses browser automation and web_fetch.
As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Implicit map keys need to be followed by map values at line 22, column 1: Manage an X/Twitter account autonomously using only OpenClaw's built-in tools. … ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 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
- 40Consistency. Frontmatter name (social-media-agent) differs from the folder (social-media-x-agent)
- 70When it triggers. States when to use, but not when not to
- 75Steps. 3 steps
- 100Tools and files. No external tools needed
- 100Execution cost. Instruction body is 45 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)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- -15SKILL.md body under 300 characters: nearly empty
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 362: enough signal without eating the budget
- +4Structure: 3 headings
- +3Step-by-step instructions: 3 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.