SKILLEMALL.ai

AC hashtag-strategy

Use to build a hashtag and discoverability strategy for social posts — the modern (2026) approach, not the outdated "30 hashtags to go viral" playbook. Run when the user says "hashtags," "what hashtags should I use," "how many hashtags," "hashtag strategy," "help me get discovered/reach," asks "do hashtags still work / are hashtags dead," or asks how to tag a post. Reads brand-profile and audience first. Sets the keyword/social-SEO layer first (caption, on-screen text, spoken audio) because that — plus engagement and the content itself — drives discovery far more than hashtags now; then picks a tight, relevant, per-platform hashtag set (respecting caps like Instagram's enforced 5-tag limit), favors niche/mid-tier tags over generic mega-tags, and designs branded/campaign hashtags for community and tracking. Rotates sets rather than copy-pasting one block, and hands placement/publishing to scheduling-and-queue. Sets honest expectations: hashtags categorize and aid search; they are not a reach hack.

ClawHub Agent Skills author: Social Media Skills v1.0.0 MIT-0 7 files body ≈ 1 205 tokens Open the sourceclawhub.ai analyzed 2 d ago

Use to build a hashtag and discoverability strategy for social posts — the modern (2026) approach, not the outdated "30 hashtags to go viral" playbook.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 6. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 30 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1205 tokens
    • medium 9 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 1011: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 30 items
    • +4Reference files are cited in the instructions (4 of 4)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a benign advisory skill for choosing hashtags and social-search keywords, with a minor note that it may read brand and audience context when invoked.
    LLM: benign (high) · VirusTotal: · 24 Jul 2026