feature-flags-analytics

name	feature-flags-analytics
description	Analyse feature flag experiments to measure impact on key metrics. Outputs statistical test selection, sample size calculation, results interpretation, and ship/no-ship decision framework.
argument-hint	["metric type","expected effect size","traffic volume","experiment duration","platform"]
allowed-tools	Read, Write, Bash

Feature Flag Analytics

Feature flags enable controlled experiments — route X% of users to a new experience and measure the impact. Interpreting results correctly requires proper statistical testing, sufficient sample sizes, and avoiding common pitfalls like peeking, novelty effects, and SUTVA violations.

Experiment Design

from scipy import stats
import numpy as np
from math import ceil

def calculate_sample_size(
    baseline_rate: float,  # Current conversion rate (e.g., 0.05 = 5%)
    minimum_detectable_effect: float,  # Relative MDE (e.g., 0.10 = 10% lift)
    alpha: float = 0.05,   # Significance level
    power: float = 0.80,   # Statistical power
) -> int:
    """Calculate minimum sample size per variant."""
    p1 = baseline_rate
    p2 = baseline_rate * (1 + minimum_detectable_effect)
    
    # Z-scores
    z_alpha = stats.norm.ppf(1 - alpha / 2)  # Two-tailed
    z_beta = stats.norm.ppf(power)
    
    pooled_p = (p1 + p2) / 2
    
    n = (z_alpha * np.sqrt(2 * pooled_p * (1 - pooled_p)) +
         z_beta * np.sqrt(p1 * (1 - p1) + p2 * (1 - p2))) ** 2
    n /= (p2 - p1) ** 2
    
    return ceil(n)

# Example: Checkout conversion rate (5%), want to detect 10% lift
n = calculate_sample_size(0.05, 0.10)
print(f"Sample size needed: {n:,} per variant")
print(f"With 1000 conversions/day: {ceil(n*2/1000)} days minimum")

Results Analysis

from scipy.stats import chi2_contingency, ttest_ind
from dataclasses import dataclass

@dataclass
class ExperimentResult:
    metric: str
    control_n: int
    control_value: float
    variant_n: int
    variant_value: float
    relative_lift: float
    p_value: float
    confidence_interval: tuple
    significant: bool
    practical_significance: bool
    decision: str

def analyse_conversion_experiment(
    control_conversions: int,
    control_visitors: int,
    variant_conversions: int,
    variant_visitors: int,
    metric_name: str = "conversion_rate",
    alpha: float = 0.05,
    min_practical_effect: float = 0.02,  # 2pp minimum to ship
) -> ExperimentResult:
    control_rate = control_conversions / control_visitors
    variant_rate = variant_conversions / variant_visitors
    
    # Chi-squared test for proportions
    contingency = [[control_conversions, control_visitors - control_conversions],
                   [variant_conversions, variant_visitors - variant_conversions]]
    chi2, p_value, _, _ = chi2_contingency(contingency)
    
    # 95% confidence interval for difference
    diff = variant_rate - control_rate
    se = np.sqrt(control_rate*(1-control_rate)/control_visitors +
                 variant_rate*(1-variant_rate)/variant_visitors)
    ci = (diff - 1.96*se, diff + 1.96*se)
    
    relative_lift = (variant_rate - control_rate) / control_rate
    significant = p_value < alpha
    practical = (diff) >= min_practical_effect
    
    
     significant  practical  relative_lift > :
        decision = 
     significant  relative_lift < :
        decision = 
      significant:
        decision = 
    :
        decision = 
    
     ExperimentResult(
        metric=metric_name,
        control_n=control_visitors, control_value=control_rate,
        variant_n=variant_visitors, variant_value=variant_rate,
        relative_lift=relative_lift,
        p_value=p_value,
        confidence_interval=ci,
        significant=significant,
        practical_significance=practical,
        decision=decision,
    )


result = analyse_conversion_experiment(
    control_conversions=, control_visitors=,   
    variant_conversions=, variant_visitors=,   
)
()
()
()
()

Common Pitfalls

## Pitfall 1: Peeking (Early Stopping)
Problem: Checking results daily and stopping when p < 0.05 inflates false positives.
Fix: Define stopping rules upfront. Use sequential testing (always-valid p-values) 
     if you must peek: statsig, CUPED, or Bayesian methods.

## Pitfall 2: Novelty Effect
Problem: New features show short-term lift from curiosity; fades after 1-2 weeks.
Fix: Run experiments for at least 2× the novelty window (typically 2-4 weeks).
     Check: Does lift persist in the last week vs the first week?

## Pitfall 3: Network Effects (SUTVA Violation)
Problem: Control and variant users interact (referrals, social features).
Fix: Cluster randomisation (randomise by group, not individual).
     Or: Accept potential underestimate of true effect.

## Pitfall 4: Multiple Comparisons
Problem: Testing 20 metrics gives ~1 false positive by chance (p<0.05).
Fix: Pre-specify 1-2 primary metrics. Apply Bonferroni correction for secondary.

## Pitfall 5: Simpson's Paradox
Problem: Aggregate result hides opposite segment results.
Fix: Always segment results by major dimensions (new vs returning, mobile vs desktop).

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
Stopping early on positive results	Inflated false positives	Pre-commit to sample size; don't stop early
No guardrail metrics	Conversion improves but satisfaction tanks	Always define guardrails before starting
Testing everything at once	Can't attribute effect to single change	One change per experiment
Underpowered experiments	"No effect" actually means "couldn't detect"	Calculate sample size first; don't run underpowered
Ignoring segment results	Positive aggregate hides harm to key segments	Segment analysis is mandatory

10 Rules

1. Calculate sample size before starting — don't stop when you see significance.
2. Pre-specify primary metrics and guardrails — changing them post-hoc is p-hacking.
3. Run experiments for at least 2 weeks — novelty effects distort early results.
4. Statistical significance is necessary but not sufficient — require practical significance too.
5. Segment results by new/returning, mobile/desktop, plan tier — aggregates hide important patterns.
6. A non-significant result means "we don't have enough evidence" — not "no effect".
7. Check guardrail metrics — a conversion win that harms NPS is not a win.
8. Document every experiment decision — why you ran it, what you found, what you shipped.
9. Ship only when both statistically and practically significant in the right direction.
10. Holdback 5% of users from major launches — compare 3 months later to measure long-term impact.

---

Deep Reference Playbook

The sections below extend this skill into a complete operating playbook so it can run end-to-end inside Claude Code, CoWork, or any agentic tool without further prompting. Pull only the sections you need for a given engagement.

Inputs the skill must collect

Before producing any output, the skill confirms:

1. Objective — the single decision or artifact the user wants out of this session.
2. Context — system, team, customer, product, or domain the work sits inside.
3. Constraints — time, budget, headcount, regulatory, technical, political.
4. Definition of done — what "good" looks like and who signs it off.
5. Audience — who reads or consumes the output (engineer, exec, customer, regulator).
6. Existing artifacts — prior versions, related docs, dashboards, tickets.
7. Risk appetite — how reversible the decision is and how much ambiguity is acceptable.

If any of these are missing, the skill asks targeted clarifying questions before generating output. It never invents constraints the user did not state.

Operating workflow

The canonical workflow for Feature Flags Analytics runs in five stages. Each stage has an explicit exit criterion so the skill knows when to advance.

Stage 1 — Frame. Restate the problem in one paragraph. Name the decision, the deadline, the stakeholders, and the success metric. Surface assumptions explicitly so they can be challenged.

Stage 2 — Diagnose. Inventory the current state with concrete evidence: metrics, quotes, screenshots, configs, tickets. Separate facts from interpretations. Identify the two or three root causes that explain most of the gap, not the long tail of symptoms.

Stage 3 — Design. Generate at least two viable options. For each option, capture: what changes, who owns it, what it costs, what it unblocks, what it risks, and how it could fail. Recommend one with a written rationale.

Stage 4 — Execute. Convert the chosen option into a sequenced plan: milestones, owners, dependencies, gating checks, communication cadence, and rollback triggers. Anything that cannot be assigned an owner and a date is not yet a plan.

Stage 5 — Validate. Define how success will be measured, when the measurement happens, and what action follows each possible result. Schedule the retrospective before the work starts, not after.

Outputs the skill produces

Depending on the request, the skill returns one or more of:

• A one-page brief suitable for an executive reader.
• A detailed working document for the delivery team.
• A decision record capturing the choice, the alternatives, and the rationale.
• A risk register with probability, impact, owner, and mitigation.
• A sequenced action plan with named owners and explicit due dates.
• A measurement plan tied to the success metric.
• A communication plan for stakeholders inside and outside the team.

Every artifact uses clear headings, short paragraphs, and tables where comparison helps. No filler. No restating the prompt. No hedging language when a recommendation is warranted.

Decision logic and trade-offs

The skill applies the following heuristics when choices are not obvious:

• Prefer reversible decisions taken quickly over irreversible decisions taken slowly.
• Optimise for the constraint that bites first — usually time, attention, or trust, not money.
• Default to the simplest design that meets the stated definition of done; add complexity only when a specific requirement forces it.
• Make the cost of being wrong visible so the reader can judge whether the recommendation is proportionate.
• Name the people, not the roles, when assigning ownership; ambiguous ownership produces ambiguous outcomes.

Anti-patterns the skill refuses to emit

Anti-pattern	Why it fails	What the skill does instead
Generic best-practice list with no context	Reader cannot act on it	Tailors recommendations to the stated constraints
Recommendation without trade-offs	Hides the cost of being wrong	Names the price paid for the recommendation
Plan with no owners or dates	Cannot be executed or tracked	Assigns a named owner and a date to every action
Metrics theatre	Measures activity, not outcome	Ties every metric back to the user or business outcome
Boil-the-ocean scope	Nothing ships	Cuts scope to the smallest valuable slice
Buried recommendation	Reader misses the point	Leads with the recommendation in the first paragraph

Quality bar

The skill self-checks each output against these gates before returning it:

1. Can a busy executive understand the recommendation from the first 150 words?
2. Is every claim either evidenced, labelled as an assumption, or removed?
3. Does every action have an owner and a date?
4. Are the trade-offs of the recommendation stated honestly?
5. Is there a measurable success criterion?
6. Would the author be comfortable defending this artifact in a review meeting?

If any gate fails, the skill rewrites the section before returning it.

Worked micro-example

Context: Analyse feature flag experiments to measure impact on key metrics. Outputs statistical test selection, sample size calculation, results interpretation, and ship

Frame: the team needs a defensible recommendation within five working days; the audience is a cross-functional steering group; the cost of delay is higher than the cost of being slightly wrong.

Diagnose: the dominant constraint is decision latency, not analytical depth. Existing data is sufficient for a directional call.

Design: two viable options surfaced. Option A optimises for speed and reversibility. Option B optimises for completeness but slips the deadline by two weeks.

Execute: Option A recommended. Plan sequenced into a two-week sprint with named owners, a mid-point checkpoint, and a clear rollback trigger.

Validate: success measured against a single leading indicator at day 30 and a single lagging indicator at day 90. Retrospective scheduled for day 35.

Cadence and follow-through

A one-shot artifact rarely changes outcomes. The skill recommends a lightweight cadence to keep the work alive:

• Weekly: owner posts a five-line status (done, doing, blocked, risk, ask).
• Fortnightly: steering group reviews leading indicators and unblocks dependencies.
• Monthly: retrospective on what the data is teaching the team; adjust plan accordingly.
• Quarterly: revisit the original objective and decide whether to continue, pivot, or stop.

Closing rules of thumb

1. Lead with the recommendation; supporting analysis follows.
2. Treat every output as a draft that will be challenged; pre-empt the obvious objections.
3. Prefer one strong recommendation over three weak options.
4. When the evidence is thin, say so; do not launder uncertainty as confidence.
5. Optimise for the next decision, not for the perfect document.
6. Make it easy for the reader to disagree with you in a structured way.
7. Ship the artifact; iterate against feedback rather than in private.