SKILLEMALL.ai

AB hyperthink

Triple-perspective deep research engine. Triggered by /hyperthink. Runs an interrogate flow to narrow scope, then executes a 6-stage fully automatic pipeline: (1) Opus master brief + 3 persona-branched prompt variants, (2) 36 parallel deep-dives (12 sections × 3 agents: Optimist / Analyst / Critic), (3a) Analyst-only trifecta audit — fact-checks Optimist, confidence-tags all claims, compresses each section to a structured comparison doc, (3b) unified synthesis narrative weaving all 3 perspectives with embedded confidence tiers, (4) executive brief + docx delivered to Telegram. Fully hands-off after trigger. Output: synthesis.md + brief.docx. Cost: ~$55–75/run.

ClawHub Agent Skills author: inxan3 v1.0.0 MIT-0 7 files body ≈ 5 662 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 71/100 · Nearly there — weak spots: when it triggers, progress reporting

AnalyzerWordTelegramInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
B
71/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

✓ Guard found no suspicious behaviour. 1 matches are attack strings quoted in this security skill's own documentation.

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5662 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "requires"

Process rating: all ten parameters 71/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5662 tokens
  • 100Steps. 55 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 17 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 668: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 55 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This is a disclosed automated research skill whose main risks are cost, long unattended runs, local persistence, and optional external notifications.
LLM: benign (high) · VirusTotal: · 29 May 2026