SKILLEMALL.ai

BB project-lifecycle-navigator

Audit a project you are unsure about and get a go, narrow, pivot, archive or stop recommendation, without writing code or changing governance state. Use when a non-technical user needs structured project guidance, a project is drifting, existing code needs a read-only audit, a recent delivery needs comparison with its current target, or new requirements may change scope. Typical triggers include 项目做了一半要不要继续, 我是不是该重开一个, 范围蔓延, 想加个新功能, I have too many projects, should I kill this one, 帮我看看这个仓库还能不能救, audit my codebase, is this project over-engineered, 定义一下 MVP, 怎样算做完, 止损, 归档, 这个项目还有价值吗, define MVP scope, scope creep, project drift, startup checklist, go or no-go, portfolio cleanup, and repository health audit. Also use for duplicate-copy detection, missing version control, hardcoded secrets in shipped artifacts, god-module and entrypoint-sprawl findings, and pre-commitment stop-loss rules. Produces bounded recommendations and handoffs without coding, self-authorizing work, changing governance state, or claiming QA acceptance. For ongoing governance with Work Orders, Milestones, and formal QA acceptance, use cms-project-governance.

ClawHub Agent Skills author: EnglandTong v2.1.1 MIT-0 15 files body ≈ 2 901 tokens Open the sourceclawhub.ai analyzed 30 h ago

Audit a project you are unsure about and get a go, narrow, pivot, archive or stop recommendation, without writing code or changing governance state.

As a process B 72/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, progress reporting

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
100
Quality 40%
50
Run on models
none yet
Process rating
B
72/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1144 chars, limit 1024

Process rating: all ten parameters 72/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 62 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2901 tokens
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 1144: 120–800 characters recommended
  • +4No input/output examples
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 62 items
  • +3Output format is stated explicitly

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.

External checks

ClawHub: clean
This is a read-only project review and planning skill with broad advisory output, but its sensitive behaviors are disclosed and bounded by user confirmation and handoff language.
LLM: benign (high) · VirusTotal: · 7 Sept 2026