SKILLEMALL.ai
RU Sign in

BD aipdlc

AIFLC PDLC Family — 11 injectable workflow packages that guide AI coding agents through professional software delivery: idea evaluation → project initiation → portfolio governance → product ownership → UX design → architecture → workspace generation → compliance → test accountability.

ClawHub Agent Skills author: Maheri v1.0.0 MIT-0 80 files body ≈ 1 102 tokens Open the sourceclawhub.ai↗ analyzed 22 h ago

AIFLC PDLC Family — 11 injectable workflow packages that guide AI coding agents through professional software delivery: idea evaluation → project initiation →…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubAI and agentsSoftware developmenttype 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
73
Run on models
none yet
Process rating
D
46/100
Unfinished process
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

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security ai-adlc/ai-adlc-rule-details/decisions/security-identity.md:238
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | Authorization failures | Access denied, privilege escalation attempt | {period} | {where} |

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (aipdlc) differs from the folder (pdlc)
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Execution cost. Instruction body is 1102 tokens

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
  • +2Single-language instructions
  • +3Description length 285: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (6 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This is a coherent local workflow skill that adds persistent agent guidance and project governance files, with no artifact evidence of exfiltration, credential theft, or destructive behavior.
LLM: benign (medium) · VirusTotal: · 9 Jul 2026