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AC aidlc

AI-Driven Development Life Cycle (AI-DLC) adaptive workflow for software development. Use when: starting a new project, new feature, bug fix, refactoring, migration, or any dev task. Chinese triggers: 新项目, 开始开发, 做个功能, 修复bug, 重构, 新需求, 新建项目. English triggers: start a project, new feature, AI-DLC, aidlc, inception, construction. Implements full AI-DLC methodology: workspace detection, adaptive requirements, user stories, workflow planning, design, code generation, and build/test.

ClawHub Agent Skills author: BingWang v1.0.0 MIT-0 28 files body ≈ 1 783 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
Has gaps
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
This is a copy of a skill from another catalog; the rating counts the canonical one: aidlc (ClawHub)

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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security references/construction/build-and-test.md:20
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition; test fixture / example file)
      - **Security tests**: Vulnerability scanning, penetration testing
      detectorfixture
    • low Risky intent intent-offensive-security references/extensions/security/baseline/security-baseline.md:269
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Security event alerting**: Alerts MUST be configured for high-value security events: repeated authentication failures, privilege escalation attempts, access from unusual locations, and authorizati

    Files scanned: 28. 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 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 76 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1783 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 481: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 76 items
    • +4Has examples (2 code blocks)

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

    External checks

    ClawHub: suspicious
    This is a coherent software-development workflow skill, but it requires broad workspace scanning and persistent verbatim logging of user input, which can retain sensitive data unexpectedly.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026