SKILLEMALL.ai

AC reckit

Bulletproof AI code verification. The agent IS the engine — no external tools required. Spawns parallel verification workers that slop-scan, type-check, mutation-test, and cross-verify before shipping. Language-agnostic. Framework-agnostic. Now with Swift/iOS support. Use when: (1) Building new projects and need verified, tested code ("build X with tests"), (2) Migrating/rebuilding codebases ("rewrite in TypeScript"), (3) Fixing bugs with proof nothing else broke ("fix this bug, verify no regressions"), (4) Auditing existing code quality ("audit this project", "how good are these tests?"), (5) Any request mentioning "reckit", "wreckit", "mutation testing", "verification", "proof bundle", "code audit", or "bulletproof". Produces a proof bundle (.wreckit/) with gate results and Ship/Caution/Blocked verdict.

ClawHub Agent Skills author: christiancattaneo v2.4.0 54 files · 24 scripts body ≈ 2 289 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
52/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
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: 54. 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 52/100

    • 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
    • 40Consistency. Frontmatter name (reckit) differs from the folder (wreckit-ralph)
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 46 steps
    • 100Execution cost. Instruction body is 2289 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 11 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)
    • +3Description length 816: 120–800 characters recommended
    • -255 emoji in the instructions: noise for the model
    • -33 of 24 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 46 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    Reckit is a coherent code-audit suite, but it asks agents to run, write, mutate, spawn workers, and sometimes commit code with weak consent boundaries and unsafe sandbox guidance.
    LLM: suspicious (high) · VirusTotal: benign · 28 May 2026