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CD hackerai-retention-analysis

Analyze HackerAI subscriber churn, renewal, and payment recovery, or design a comparison of how a product or model change affects paid retention. Distinguish early experience warnings from mature billing outcomes using aggregate evidence. Not for changing billing or routing, or reading customer conversations.

hackerai-tech/hackerai Agent Skills author: hackerai-tech NOASSERTION 3 files body ≈ 617 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Analyze HackerAI subscriber churn, renewal, and payment recovery, or design a comparison of how a product or model change affects paid retention.

As a process D 42/100 · Unfinished process — References files that are not bundled: ../hackerai-user-research/SKILL.md

AnalyzerData and analyticsFinanceSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
42/100
Unfinished process
References files that are not bundled: ../hackerai-user-research/SKILL.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 3. 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")
  • warning missing-ref reference to a missing file: ../hackerai-user-research/SKILL.md

Process rating: all ten parameters 42/100

Will not run. References files that are not bundled: ../hackerai-user-research/SKILL.md
  • 0Tools and files. 1 referenced file(s) missing: ../hackerai-user-research/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 617 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
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 310: enough signal without eating the budget
  • +4Structure: 3 headings
  • +3Step-by-step instructions: 5 items
  • +4Reference files are cited in the instructions (1 of 1)

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