SKILLEMALL.ai

AB receive-feedback

Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.

ClawHub Claude Code author: Kevin Anderson v1.0.0 MIT-0 6 files body ≈ 2 635 tokens Open the sourceclawhub.ai analyzed 11 h ago

Process external code review feedback with technical rigor.

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ProcedureSoftware developmentAI 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%
91
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
50
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: 6. 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 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (read) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 100Steps. 48 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2635 tokens
    • 100Running it twice. No mutating operations
    • low 12 top-level sections: this looks like several domains in one skill
    • 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 190: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 48 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill gives an agent a structured, confirmation-based workflow for verifying and applying code review feedback, with no hidden scripts or unrelated data access found.
    LLM: benign (high) · VirusTotal: · 1 Jun 2026