SKILLEMALL.ai

AC build-protocol-decision

Rigorous workflow for high-stakes decision-making: investments (stocks/crypto/real-estate), major purchases, technology selection, supplier choice. Use when the decision commits >$1000 or >1 day of effort, is hard to reverse, or involves tradeoffs between multiple alternatives. Inherits build-protocol core rules, adds decision-specific: real-time data verification mandate (never trust document prices), methodology transparency (PE/DCF/MA required, no gut calls), position-sizing discipline, stop-loss as first-class rule, daily P/L tracking. Guards against 'Sycophancy of Precision'—writing '+12.85%' looks trustworthy but precision ≠ accuracy. Triggers on: 'should I buy/invest in X', 'compare options', 'pick a vendor', '选哪个', '投资策略', '操盘手册', 'which option', 'buy vs build', '怎么选'.

ClawHub Agent Skills author: Christianye v1.0.1 MIT-0 5 files · 1 script body ≈ 2 441 tokens Open the sourceclawhub.ai analyzed 2 d ago

Rigorous workflow for high-stakes decision-making: investments (stocks/crypto/real-estate), major purchases, technology selection, supplier choice.

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureResearchPeople and hiringtype 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
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 5 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 85Steps. 65 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2441 tokens
    • 100Progress reporting. Reports progress
    • low 10 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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 787: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 65 items
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: clean
    This skill is a transparent decision-analysis workflow with an optional local audit script, and I found no hidden persistence, credential access, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 10 Jun 2026