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BC earnings-preview-single

Generate a concise 4-5 page equity research earnings preview for a single company. Analyzes the most recent earnings transcript, competitor landscape, valuation, and recent news to produce a professional HTML report.

w95/awesome-claude-corporate-skills Agent Skills author: w95 MIT 3 files body ≈ 9 262 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate a concise 4-5 page equity research earnings preview for a single company.

As a process C 51/100 · Has gaps — weak spots: result and completion, consistency, execution cost

GeneratorData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
63
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Consistency w 8
40
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. 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
  • low Secrets in code secret-high-entropy-token report-template.md:24
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    <script src="https://cdn.jsdelivr.net/npm/ch…@….1.0/dist/chartjs-plugin-annotation.min.js" integrity="sha3…ChN+uaTZ…KYp/SjBNPxa92+hM7EAY" cro
    quoted

Files scanned: 3. 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")
  • warning body-long SKILL.md body ≈ 9262 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 37 mutating operations with no state check
  • 40Consistency. Frontmatter name (earnings-preview-single) differs from the folder (spglobal-earnings-preview)
  • 40Execution cost. Instruction body is 9262 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 154 steps, 3 vague phrases
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 216: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 154 items
  • +4Has examples (13 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.