SKILLEMALL.ai

BC zettel-note

The note-writing discipline for this vault's evergreen knowledge graph, modeled on a Zettelkasten reading companion and governed by the vault conventions. Enforces declarative-claim titles, one claim per note (atomicity), own-words prose with no block quotes, the piped [[slug|Title]] link form, the labeled link-relationship vocabulary (Confirms/Contradicts/Extends/Context/Prerequisite/Builds-on/Applies/Example-of/Contrasts-with), 3-6 links per note, and search-before-create deduplication. Use when capturing a claim as an evergreen note, refactoring a sprawling note into atomic ones, or wiring a new claim into the graph. Trigger keywords — evergreen note, zettel, atomic note, declarative title, link relationships, search before create, route the claim.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 1 863 tokens Open the sourcegithub.com↗ analyzed 16 h ago

The note-writing discipline for this vault's evergreen knowledge graph, modeled on a Zettelkasten reading companion and governed by the vault conventions.

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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: 1. 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
    • 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. 4 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1863 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 761: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.