SKILLEMALL.ai

BD company-classification-routing

Classifies a company and routes it to the correct analytical treatment — life cycle stage, sector type, earnings status, ownership, distress markers, geography — then compiles the hard constraints that follow into classification.json. Use before any valuation begins, when deciding which valuation model applies, or when a company looks non-standard — a bank or insurer, a pre-revenue or loss-making firm, a distressed or declining business, a private company or an IPO, a cyclical or commodity producer, an emerging-market firm, or a multi-business group.

lyndonkl/claude Agent Skills author: lyndonkl 6 files body ≈ 7 055 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Classifies a company and routes it to the correct analytical treatment — life cycle stage, sector type, earnings status, ownership, distress markers…

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware developmentFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 0

✓ No critical or high findings

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7055 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7055 tokens
  • 100Steps. 38 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 556: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.