SKILLEMALL.ai

BC hienergy-advertiser-intelligence-affiliate-copilot

Official Hi Energy AI skill for finding and managing affiliate marketing programs, affiliate deals/offers, commissions, transactions, and partner contacts in OpenClaw. Query HiEnergy API v1 for advertisers, affiliate programs, deals, transactions (with analytics meta), contacts, status changes, agencies, tags/categories, and publisher details. Best for affiliate program discovery, affiliate deal research, partner marketing operations, advertiser lookup, brand intelligence, publisher contacts, transaction analytics (sales, commissions, trends), commission analysis, and domain-to-advertiser search across networks like Impact, Rakuten, CJ, Awin, Partnerize, and ShareASale. Includes deep advertiser profile (show endpoint) responses with links such as https://app.hienergy.ai/a/<advertiser_id>. Learn more: https://www.hienergy.ai and https://app.hienergy.ai/api_documentation.

modbender/skill-library-mcp Agent Skills author: modbender MIT 30 files · 1 script body ≈ 1 221 tokens Open the sourcegithub.com analyzed 32 h ago

Official Hi Energy AI skill for finding and managing affiliate marketing programs, affiliate deals/offers, commissions, transactions, and partner contacts in…

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

IntegrationData and analyticsMarketingAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
99
Quality 40%
72
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-password-literal scripts/hienergy_skill.py:1652
    Hard-coded password / key literal (may be an example)
    api_key = sys.argv[1]

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • 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 62/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 9 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 46 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1221 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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)
  • +3Description length 882: 120–800 characters recommended
  • -311 of 13 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 46 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.