SKILLEMALL.ai

AB NextFromKnowledge

Knowledge-to-action skill that turns notes, research, meeting summaries, documents, and knowledge graph outputs into the next action, decision, plan, or experiment. Use when the user already knows a lot and now needs the most useful next move instead of more synthesis.

ClawHub Agent Skills author: haidong v1.0.0 MIT-0 12 files · 1 script body ≈ 1 756 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 73/100 · Nearly there — weak spots: consistency, running it twice, progress reporting

ProcedureMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
73/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Consistency w 8
40
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: 11. 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 "slug"

    Process rating: all ten parameters 73/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (NextFromKnowledge) differs from the folder (next-from-knowledge)
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 4 branches
    • 85Steps. 128 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 1756 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 269: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 128 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

    External checks

    ClawHub: clean
    The skill's code, runtime instructions, and requirements align with its stated purpose of turning knowledge into concrete next actions; there are no unexpected credentials, network calls, or install-time downloads.
    LLM: benign (high) · VirusTotal: benign · 2 Apr 2026