SKILLEMALL.ai

AD myr

Capture, search, verify, export, import, and synthesize Methodological Yield Reports (MYRs) for Starfighter/Pistis intelligence compounding. Use when: (1) installing MYR on a node, (2) storing yield from OODA cycles, (3) searching prior yield before new work, (4) operator-reviewing MYR quality, (5) exporting/importing signed MYRs between nodes, (6) generating weekly digests, or (7) integrating MYR with an agent memory system. Triggers: "install MYR", "store a MYR", "what did we learn about", "weekly yield", "export yield", "import yield", "methodological yield", "MYR".

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 974 tokens Open the sourcegithub.com analyzed 2 d ago

Capture, search, verify, export, import, and synthesize Methodological Yield Reports (MYRs) for Starfighter/Pistis intelligence compounding.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerResearchData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 85Steps. 42 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1974 tokens
    • 100Progress reporting. Reports progress
    • low 15 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 575: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.