SKILLEMALL.ai

AC build-protocol-engineering

Rigorous workflow for software engineering projects (design docs, code, deployment). Use when building a multi-module system, API service, or infrastructure project requiring design documents (D1-DN), code review, testing, and deployment. Inherits core rules from build-protocol (≤2 parallel for tightly-coupled code paths, Audit unmissable, Why This Way), adds engineering-specific: 3-layer consistency check (naming↔business↔data), API contract validation, deployment runbook requirement, rollback plan requirement. Machine-verifies: type consistency across layers, API path matches between frontend/backend, database schema matches TypeScript interfaces, env var consistency across config/code/deployment. Triggers on: 'design document', 'API spec', 'deploy plan', 'multi-service system', 'build service', '做设计文档 / 做系统'.

ClawHub Agent Skills author: Christianye v1.0.1 MIT-0 5 files · 1 script body ≈ 1 856 tokens Open the sourceclawhub.ai analyzed 30 h ago

Rigorous workflow for software engineering projects (design docs, code, deployment).

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 5. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 12 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 25 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1856 tokens
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Description length 823: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a disclosed engineering workflow skill with a local audit script; it is process-heavy but not deceptive or destructive.
    LLM: benign (high) · VirusTotal: · 10 Jun 2026