SKILLEMALL.ai

AB think-like-fable

An operating manual for rigorous reasoning — how to read the real request beneath the words, decompose into independently-checkable pieces, spend effort where the risk lives, verify by re-derivation instead of plausibility, label known vs. guessed, attack your own conclusion, and communicate answer-first. Apply it to the task at hand; it changes HOW you work, not WHAT you do. Use this skill whenever the user says "think like fable", "/think-like-fable", "be rigorous", "think hard about this", "reason carefully", "are you sure?", "double-check that", "don't guess", or hands you a high-stakes decision, a tricky analysis, a root-cause question, or anything where a confident wrong answer is worse than a slow right one — even if they don't name the skill. Do not load it for trivial mechanical edits or casual questions.

ClawHub Agent Skills author: Dennis Rongo v1.0.0 MIT-0 2 files body ≈ 2 496 tokens Open the sourceclawhub.ai analyzed 4 d ago

An operating manual for rigorous reasoning — how to read the real request beneath the words, decompose into independently-checkable pieces, spend effort where…

As a process B 67/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
67/100
Nearly there
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: 2. 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 67/100

    • 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. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 19 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2496 tokens
    • 100Progress reporting. Reports progress
    • low 11 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 825: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 19 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    This is a reasoning-style skill that broadens when an agent should be careful, but it does not request files, credentials, network access, persistence, or execution authority.
    LLM: benign (high) · VirusTotal: · 16 Aug 2026