SKILLEMALL.ai

AC software-requirements-engineering

Book-aligned enterprise software requirements engineering based on Wiegers & Beatty, Software Requirements, 3rd Edition. Use when Codex must act like a professional requirements engineering team for full-lifecycle or phase-specific work: business requirements and vision/scope, customer-development partnership, stakeholder/user-class analysis, product champions, requirements elicitation/需求获取, use cases, user stories, business rules, requirements analysis/modeling/需求分析建模, prototypes, prioritization, SRS drafting or review/需求规格说明, excellent requirement writing, quality attributes/NFRs, requirements validation/需求验证, acceptance criteria, requirements reuse, baselines, version/status tracking, change control, impact analysis, traceability, requirements management/需求管理, agile/enhancement/packaged/outsourced/BPA/analytics/embedded project tailoring, enterprise governance, process improvement, risk management, or production-quality requirements documentation.

ClawHub Agent Skills author: Yifan Zhu v1.0.0 MIT-0 14 files body ≈ 2 978 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

ProcedureOperations and projectsData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
60/100
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: 14. 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

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (software-requirements-engineering) differs from the folder (software-requirements-engineering-skill)
    • 85Steps. 80 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Result and completion. Output format and completion criterion are stated
    • 100Execution cost. Instruction body is 2978 tokens
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 964: 120–800 characters recommended
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 80 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (11 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: clean
    This skill is a requirements-engineering guide made of Markdown references and a small YAML prompt, with no evidence of hidden execution or data misuse.
    LLM: benign (high) · VirusTotal: · 29 May 2026