SKILLEMALL.ai

AF cn-stock-move-reason

Use when analyzing why one A-share stock moved sharply using Codex, without Gemini, from Eastmoney quote data, announcements, Eastmoney 股吧/资讯 posts, Eastmoney Guba topic heat, Sohu index/sector context, and A-share breadth.

ClawHub Agent Skills author: tóng v0.1.0 MIT-0 5 files body ≈ 3 994 tokens Open the sourceclawhub.ai analyzed 3 d ago

Use when analyzing why one A-share stock moved sharply using Codex, without Gemini, from Eastmoney quote data, announcements, Eastmoney 股吧/资讯 posts, Eastmoney…

As a process F 63/100 · Will not run — References files that are not bundled: references/sentiment-framework.md

AnalyzerSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
F
63/100
Will not run
References files that are not bundled: references/sentiment-framework.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/sentiment-framework.md

Process rating: all ten parameters 63/100

Will not run. References files that are not bundled: references/sentiment-framework.md
  • 0Tools and files. 1 referenced file(s) missing: references/sentiment-framework.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 65Failures and branches. 3 branches
  • 100Steps. 55 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3994 tokens

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 223: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 55 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

External checks

ClawHub: suspicious
The skill mostly matches its stock-analysis purpose, but it should be reviewed because it can persist learning changes to local skill files and coordinate with optional private/account-related skills.
LLM: suspicious (high) · 3 Sept 2026