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Ln 72 Product Outcome Evaluator

Community
levnikolaevich
ln-72-product-outcome-evaluator

Evaluates observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-72-product-outcome-evaluator
Stars
565
Forks
84
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by levnikolaevich on GitHub. Read the source before you install it.

Installation

Install the Ln 72 Product Outcome Evaluator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/operations-suite/skills/ln-72-product-outcome-evaluator .claude/skills/ln-72-product-outcome-evaluator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 72 Product Outcome Evaluator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ln 72 Product Outcome Evaluator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ln 72 Product Outcome Evaluator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Product Outcome Evaluator

Goal: Determine what available evidence supports about a delivered product outcome and recommend continuation, adjustment or stopping. Remain read-only: do not change instrumentation, experiments, user treatment, campaigns or product files.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Original hypothesisProduct intent, baseline, experiment/measurement plan and accepted targetsReconstruct from attributable sources; keep missing targets unknown
Outcome evidenceAuthorized analytics, experiment results, customer behavior and cost/support evidenceSanitized exports with explicit measurement limits
AnalysisReproducible queries/statistics appropriate to the study designTransparent arithmetic and qualitative inference; no fabricated causal confidence

Domain Rules

  • Distinguish delivered behavior, observed metric movement and causal product impact. A release or acceptance test proves neither adoption nor business value.
  • Do not choose success thresholds after seeing the result. Separate predeclared criteria from exploratory findings and owner preferences.
  • Use only authorized data with necessary minimization. A recommendation is not permission to run an experiment or contact users.

Checklist

1. Frame the Outcome Decision

  • Resolve the delivered capability, intended audience, original hypothesis, decision horizon and outcome decision requested.
  • Identify the released/deployed version, rollout/exposure window and relevant baseline or comparison group.
  • Recover predeclared primary metrics, guardrails, targets and stop rules; mark absent criteria rather than inventing them.
  • Separate product intent and owner preference from measured behavior and external assumptions.

2. Assess Measurement Fitness

  • Inspect metric definitions, units, denominators, event coverage, deduplication, identity joins and missing data.
  • Check whether users were actually exposed and whether observation duration supports the intended outcome.
  • Assess cohort composition, selection bias, seasonality, concurrent changes and other confounders.
  • For experiments, inspect assignment, contamination, sample imbalance and uncertainty using the actual study design.
  • Distinguish trustworthy measurements, reported results, estimates, qualitative signals and unavailable evidence.

3. Evaluate Value and Harm

  • Compare outcomes with valid baselines or controls using reproducible calculations and appropriate uncertainty.
  • Check guardrails and material regressions in user experience, reliability, support burden, cost or data quality.
  • Separate aggregate effects from relevant segments and expose tradeoffs without fishing for favorable subgroups.
  • Distinguish causal conclusions supported by the design from correlations and exploratory interpretations.
  • Identify whether failure lies in adoption, interaction, correctness, measurement or the original value hypothesis.

4. Recommend the Next Decision

  • Recommend continue, adjust or stop only to the degree supported by the evidence; explain what could reverse the recommendation.
  • For uncertainty, define the cheapest next measurement or experiment with audience, signal, boundary and decision criterion without executing it.
  • Return results linked to the original requirement/hypothesis and observed deployment state.
  • Report data and causal limitations explicitly; do not transform lack of proof into proof of no effect.

Verdict

  • SUPPORTED: evidence supports the intended outcome within the stated population, window and causal limits.
  • NOT_SUPPORTED: valid evidence contradicts the declared outcome or violates a required guardrail.
  • INCONCLUSIVE: evidence cannot establish the outcome or causal interpretation.
  • BLOCKED: essential hypothesis, exposure identity or authorized data is unavailable.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Hypothesis, deployed exposure, baseline/control, metric definitions and quality, reproducible results and uncertainty, guardrails, causal limits, recommendation and next evidence action.

Frequently asked questions

What does the Ln 72 Product Outcome Evaluator AI skill do?

Evaluates observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.

Why use Ln 72 Product Outcome Evaluator on TypingMind?

Because you install it once and use it with any model. Ln 72 Product Outcome Evaluator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ln 72 Product Outcome Evaluator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/operations-suite/skills/ln-72-product-outcome-evaluator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 72 Product Outcome Evaluator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ln 72 Product Outcome Evaluator?

As many as you like. As long as a model supports skills, you can use Ln 72 Product Outcome Evaluator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 72 Product Outcome Evaluator AI skill free?

Yes. It is published on GitHub by levnikolaevich under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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