SKILLEMALL.ai

BD HillClimb

Improves anything in LifeOS (a skill, workflow, eval, ISA, prompt, rule or code metric) by experiment: finds its real misses in Errata, Assay and its ISA, draws changes from varied proposers, keeps only what held-out measurement supports until plateau, lands each catch as a standing check, then verifies complaints fell. With no target it climbs the steepest hill. USE WHEN hill climb, hillclimb, climb this, improve X, make X better, optimize, reduce latency, improve a metric, experiment, iterate, hypothesis, evolve ideas, ideate, experiment ideas, this keeps failing fix it for good, what hurts most, what should we improve, turn this miss into a check, did that fix work, before and after, iterate with me, multiple passes, revisit, rework. NOT FOR filing a complaint (Errata), writing or calibrating one eval suite (Assay), editing an ISA (ISA), skill scaffolding or description tuning (CreateSkill), mining outside content (Upgrade), one-pass brainstorming (BeCreative), diagnosis without an experiment (Science).

danielmiessler/Personal_AI_Infrastructure Agent Skills author: danielmiessler 15 files · 2 scripts body ≈ 4 528 tokens Open the sourcegithub.com↗ analyzed 5 h ago

Improves anything in LifeOS (a skill, workflow, eval, ISA, prompt, rule or code metric) by experiment: finds its real misses in Errata, Assay and its ISA…

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticsCustomer supportSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
45/100
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "evals"

    Process rating: all ten parameters 45/100

    • 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
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4528 tokens
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (6 tags): a typed call is more reliable

    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
    • +3Description length 1021: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.