SKILLEMALL.ai

BC Competitor Watch

Competitor social media content strategy analyzer. Feed it a JSON file of any competitor's posts and get a full strategy breakdown — topic distribution, content style analysis (listicle/story/contrarian/how-to/data-driven/thread), posting schedule, top-performing content patterns, and exploitable gaps (topics and styles they DON'T cover). Works with any platform's exported data. No API keys needed. Zero external dependencies.

ClawHub Agent Skills author: Lucius Pang v1.0.3 MIT-0 3 files body ≈ 320 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerInfrastructureMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
56/100
Has gaps
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 56/100

  • 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
  • 40Consistency. Frontmatter name (Competitor Watch) differs from the folder (phy-competitor-watch)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 7 steps
  • 100Execution cost. Instruction body is 320 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
  • +2Single-language instructions
  • +3Description length 429: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 7 items
  • +4Has examples (2 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

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

External checks

ClawHub: clean
This skill is a local competitor-post analyzer that reads user-provided JSON and prints reports without hidden network, credential, persistence, or mutation behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026