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BC competitive-ops

AI competitive intelligence pipeline -- analyze competitors, generate reports, track changes

ClawHub Hermes author: dalianmao000 v1.0.0 MIT-0 79 files body ≈ 5 667 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 63/100 · Has gaps — weak spots: when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
79/100
safety, quality, tests
Safety 60%
100
Quality 40%
48
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
  3. 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 · 0

✓ No critical or high findings

Files scanned: 49. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 92 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • warning body-long SKILL.md body ≈ 5667 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 63/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5667 tokens
  • 85Steps. 211 steps, 1 vague phrases
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 22 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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 92: 120–800 characters recommended
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • -33 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 211 items
  • +3Output format is stated explicitly
  • +4Has examples (21 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.

External checks

ClawHub: suspicious
The competitive-intelligence workflow is mostly coherent, but the package includes under-disclosed extra agent capabilities and persistent automation that should be reviewed before installation.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026