Featbit Experimentation

FeatBit Experimentation

This skill is the single entry point for the FeatBit release-decision loop:

intent -> hypothesis -> implementation -> exposure -> measurement -> interpretation -> decision -> learning -> next intent

It replaces the old multi-skill layout (featbit-release-decision, intent-shaping, hypothesis-design, reversible-exposure-control, measurement-design, experiment-workspace, evidence-analysis, learning-capture, and project-sync). Do not call those split skills from this workflow. Treat their former behavior as internal stages of this skill.

Core Principles

• Do not let an available tool define the user's problem before the decision type is clear.
• Do not implement before the business intent and hypothesis are explicit.
• Do not expose a measurable change before reversibility and rollout rules are defined.
• Do not measure with multiple primary metrics. One metric decides; guardrails protect.
• Do not interpret experiment data before sufficiency, SRM, instrumentation, and window checks pass.
• Do not close a cycle without recording a reusable learning and a next hypothesis.
• Persist every stage transition through the configured FeatBit experimentation MCP server.

MCP Contract

The FeatBit API database is the canonical source for experiment state. Read it on entry and write it before moving stages.

Required MCP tools:

The MCP client configuration must provide normal FeatBit auth headers: Authorization, Organization, and Workspace. Do not ask for or pass a per-experiment access token.

For the canonical tool inventory and step-by-step usage rules, read references/mcp-tool-usage.md before executing MCP-backed experiment work.

Feature flag mutation safety:

• Before calling featbit_experiment_create_feature_flag, featbit_experiment_update_feature_flag_targeting, or featbit_experiment_toggle_feature_flag, summarize the exact flag key, environment implied by the experiment, operation, rollout/toggle state, and rollback consequence, then ask the user for explicit approval.
• Set confirmedByUser: true in the MCP request only after the user clearly approves that exact operation. Do not infer approval from earlier setup discussion, a stage transition, or an analysis recommendation.
• If the user has not approved, stop before the MCP mutation and state the proposed operation awaiting approval.

Run traffic configuration safety:

• featbit_experiment_update_run_traffic changes how experiment evidence is read; it does not mutate the live feature flag or who sees a variation.
• On a draft run, configure traffic directly after explaining the intended analysis sample.
• Use featbit_experiment_list_layers before assigning a run to a layer. If the layer does not exist, create it only after the user explicitly approves.
• Use sliceStart and sliceEnd for layer bucket ranges such as 30 to 60; layerTrafficPercent is the slice width. Avoid legacy trafficPercent / trafficOffset unless preserving older run behavior.
• If a run is already collecting, analyzing, or decided, summarize the exact run, control/treatment roles, layer eligibility, and sampling rates, then ask the user to approve before passing confirmedByUser: true.
• Choose analysisSamplingPlan include rates from the actual exposure distribution in the run window: includeRate = desired analyzed users for that variation / observed served users for that variation * 100, capped at 100. If the live flag rollout changed after data was collected, start a new run window or collect fresh data instead of reusing the old distribution.

Layer mutation safety:

• Creating, updating, or archiving a registered layer can affect future mutual-exclusion assignments. Summarize the layer name, key, assignment unit selector, status, and active run impact before asking for approval.
• Set confirmedByUser: true only after approval for that exact layer operation.

Valid values:

Entry Protocol

At minimum, pass the experiment id:

/featbit-experimentation <experiment-id>

The web UI may also pass a stage hint and selected run:

/featbit-experimentation <experiment-id> --stage intent-hypothesis
/featbit-experimentation <experiment-id> --stage exposure
/featbit-experimentation <experiment-id> --stage measuring
/featbit-experimentation <experiment-id> --stage decision --run-id <run-id>
/featbit-experimentation <experiment-id> --stage learning

Parse experiment-id, optional stage, optional run-id, and optional unresolved UI item labels from the invocation. If experiment-id is missing, ask for it before proceeding.

Before asking or saying anything, call:

state = MCP("featbit_experiment_get_experiment", experimentId=experiment_id)

If the MCP call fails because the server is unreachable, retry once. If the retry still fails, treat this as a blank new project and proceed without diagnosing the database to the user. If the user later asserts that the database is reachable, call the read tool again before saying otherwise.

After the MCP read resolves featbitProjectKey, use product-context MCP capabilities if available. If none are configured, skip product facts silently. Never hardcode local product URLs.

UI Stage Prompts

When the user comes from the FeatBit Release Decision UI, do not require them to copy a long stage prompt. A short invocation is enough:

Use featbit-experimentation for experiment <experiment-id> at stage <stage>.

If a UI stage is explicit, read references/ui-stage-prompts.md and apply that stage protocol. Treat unresolved UI item labels as private routing hints only; do not recite them to the user.

Supported UI stages:

First Response Rules

If the project is blank, ask only:

What are you trying to improve or learn?

Do not enumerate empty fields, announce state loading, or explain the stage.

If messages is non-empty, emit no greeting or recap. The user can already see the conversation history. Wait for their next prompt.

If meaningful state exists and messages is empty, summarize only the non-empty fields needed for the next decision in at most two short sentences.

If entryMode == "expert", the user already filled the setup wizard. Do not run blank intent, hypothesis, or measurement shaping unless explicitly asked. Acknowledge the configured method, metrics, priors, variants, window, and any experimentRuns[*].inputData. If inputData exists, route directly to analysis.

Stage Routing

Infer the stage from state plus the user's message. Multiple signals can apply; choose the earliest stage that blocks a sound decision.

Execution Procedure

def on_session_start(argv, user_message):
    experiment_id = parse_experiment_id(argv)
    ui_stage = parse_stage_hint(argv, user_message)
    run_id = parse_run_id(argv, user_message)
    assert experiment_id, "experiment-id is required"
    state = read_state_with_one_retry(experiment_id)
    if ui_stage:
        stage_protocol = read("references/ui-stage-prompts.md")
        return route_ui_stage(ui_stage, experiment_id, run_id, state, stage_protocol)
    if state.status == "unavailable" or is_blank_project(state):
        ask_user("What are you trying to improve or learn?")
        return

    if state.featbitProjectKey and MCP.has_tool("featbit_product_context_get_facts"):
        _product_facts = MCP("featbit_product_context_get_facts", projectKey=state.featbitProjectKey)

    if state.messages:
        return  # no visible recap

    if state.entryMode == "expert":
        acknowledge_expert_setup(state)
        if any(run.inputData for run in state.experimentRuns):
            manage_experiment(experiment_id, "run analysis")
        return

    say(summarize_nonempty(state, max_sentences=2))
    ask_user("What would you like to work on next?")

def on_user_turn(experiment_id, user_message):
    state = MCP("featbit_experiment_get_experiment", experimentId=experiment_id)
    lens = infer_cf_lens(state, user_message)
    if lens == "CF-01": return shape_intent(experiment_id, user_message, state)
    if lens == "CF-02": return design_hypothesis(experiment_id, user_message, state)
    if lens in ("CF-03", "CF-04"): return control_exposure(experiment_id, user_message, state)
    if lens == "CF-05": return design_measurement(experiment_id, user_message, state)
    if lens in ("CF-05-RUN", "CF-06-RUN"): return manage_experiment(experiment_id, user_message, state)
    if lens in ("CF-06", "CF-07"): return analyze_evidence(experiment_id, user_message, state)
    if lens == "CF-08": return capture_learning(experiment_id, user_message, state)
    answer_directly(user_message, state)

CF-01: Shape Intent

Use when the user has a desire or tactic but no measurable business outcome.

Read references/intent-goal-extraction-patterns.md.

Actions:

• If the user leads with a solution, ask what outcome that solution should produce.
• Extract the specific behavior or metric, audience, baseline, and iteration scope.
• Ask one question at a time.
• Do not proceed to hypothesis or implementation until the goal is measurable.

Persist:

MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "goal": goal,
    "intent": original_user_framing,
    "lastAction": "Intent clarified",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="intent")

When the user asks what next: tell them to mark CF-01 satisfied in the UI, then continue with CF-02 in the same Intent & Hypothesis stage.

CF-02: Design Hypothesis

Use when a goal exists but the causal claim is missing or vague.

Read references/hypothesis-template.md.

Template:

We believe [change X] will [move metric Y in direction Z] for [audience A], because [causal reason R].

Actions:

• Require change, metric, direction, audience, and causal reason.
• Ask: "Under what conditions would we conclude this hypothesis was wrong?"
• Do not persist primaryMetric here. A complete primary metric belongs to CF-05.
• Do not proceed to exposure planning until all five components are present.

Persist:

MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "hypothesis": hypothesis,
    "change": change,
    "variants": variants,
    "lastAction": "Hypothesis formed",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="hypothesis")

When the user asks what next: mark Intent & Hypothesis satisfied, move to Exposure, then run CF-03/04.

CF-03 / CF-04: Control Exposure

Use when a change needs reversibility, flag contract, targeting, rollout, rollback, or traffic allocation rules.

Read:

• references/exposure-pm-dev-handoff.md
• references/exposure-mcp-lifecycle.md
• references/exposure-rollout-patterns.md
• references/exposure-multi-experiment-traffic.md
• references/exposure-tool-featbit-webui.md only when the user needs concrete FeatBit UI operation guidance.

Default output is a concrete feature flag and exposure contract: flag name, stable key, type, variants, evaluation point, variant behavior, target audience, protected users, initial rollout, expansion checkpoints, stop conditions, rollback triggers, and metric-event requirements.

Rules:

• Confirm a hypothesis exists before flag work.
• Do not ask who should create the flag; define the contract and the UI/automation next step.
• Treat FeatBit as the source of truth for flag binding. Experiment constraints may propose a flag key, but the flag is not bound until featbit_experiment_get_feature_flag succeeds or featbit_experiment_create_feature_flag succeeds and is read back.
• Do not create or mutate a flag until the required contract fields are known: flag name, key, type, variants, default/off variation, target audience, protected audience, rollout split, rollback trigger, and dispatch key when rollout is percentage-based.
• If feature-flag MCP tools are available, follow references/exposure-mcp-lifecycle.md: read existing flags with featbit_experiment_get_feature_flag; if the tool returns ResourceNotFound, create the missing flag with featbit_experiment_create_feature_flag; read the created flag back; configure rollout/targeting with featbit_experiment_update_feature_flag_targeting; then call featbit_experiment_toggle_feature_flag only when exposure should begin now or a run is moving to collecting.
• Targeting updates do not enable a flag. If the user asks to start exposure, launch, collect, expand, pause, or rollback and the toggle tool is available, operate the flag toggle through featbit_experiment_toggle_feature_flag; do not send the user to the FeatBit UI only to switch the flag on or off.
• Create, targeting update, and toggle are production-impacting mutations. Ask for explicit user approval immediately before each mutation and pass confirmedByUser: true only for the approved call.
• Direct targeting update is the default MCP path. Use change-request mode only when the user supplies reviewer ids, explicitly asks for approval/change-request mode, or the local operating policy requires it.
• If a required flag field is missing or invalid, ask for that field before calling the flag MCP tool. Do not invent reviewer ids, user segments, or production targeting rules.
• Do not set Exposure as satisfied, do not say the flag is bound, and do not advance from CF-03/04 when get_feature_flag still returns ResourceNotFound.
• Never start at 100% unless protected-audience targeting is explicit.
• For multiple experiments on the same flag or surface, classify as sequential, mutual-exclusion, or orthogonal. Sample size must use the actual traffic slice.

Persist:

if MCP.has_tool("featbit_experiment_get_feature_flag"):
    flag = MCP("featbit_experiment_get_feature_flag", experimentId=experiment_id, key=flag_key)
    if flag.error == "ResourceNotFound":
        require_explicit_user_approval("create feature flag", flag_contract)
        flag_contract["confirmedByUser"] = True
        MCP("featbit_experiment_create_feature_flag", experimentId=experiment_id, request=flag_contract)
        flag = MCP("featbit_experiment_get_feature_flag", experimentId=experiment_id, key=flag_key)
    assert not flag.error, "feature flag must exist before Exposure can be satisfied"
    require_explicit_user_approval("update feature flag targeting", targeting)
    MCP("featbit_experiment_update_feature_flag_targeting", experimentId=experiment_id, key=flag.key, request={
        "confirmedByUser": True,
        "revision": flag.revision,
        "targeting": targeting,
        "comment": "Initial experiment rollout",
        # Optional only when approval is required:
        # "useChangeRequest": True,
        # "reviewers": reviewer_ids,
        # "reason": "Initial experiment rollout"
    })
    if exposure_should_start_now and MCP.has_tool("featbit_experiment_toggle_feature_flag"):
        require_explicit_user_approval("enable feature flag", {"key": flag.key, "isEnabled": True})
        MCP("featbit_experiment_toggle_feature_flag", experimentId=experiment_id, key=flag.key, request={
            "confirmedByUser": True,
            "isEnabled": True,
            "comment": "Enable flag for initial experiment exposure",
        })
        flag = MCP("featbit_experiment_get_feature_flag", experimentId=experiment_id, key=flag.key)
        assert flag.isEnabled, "feature flag must be enabled before collecting exposure data"
MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "constraints": flag_contract_and_rollout_with_actual_flag_key_and_variations,
    "lastAction": "Exposure contract defined",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="implementing")

When the user asks what next: mark Exposure satisfied, create/configure the flag through FeatBit MCP when available, otherwise use the FeatBit UI using the contract, then continue to Measuring / CF-05.

CF-05: Design Measurement

Use when the hypothesis exists but metrics, guardrails, or instrumentation are incomplete.

Read:

• references/measurement-event-schema-design.md
• references/measurement-tool-featbit-sdk.md
• references/exposure-multi-experiment-traffic.md when traffic is split across experiments.

Rules:

• One primary metric only. Everything else is a guardrail or diagnostic.
• metricName, metricEvent, metricType, metricAgg, and expectedDirection are required.
• Primary expectedDirection is increase_good or decrease_good.
• Guardrail direction is increase_bad or decrease_bad.
• Keep guardrails to 2-3.
• Do not advance to measuring until instrumentation is confirmed.

Persist metrics through featbit_experiment_update_metrics, not update_experiment:

MCP("featbit_experiment_update_metrics", experimentId=experiment_id, update={
    "metricName": primary_metric.name,
    "metricEvent": primary_metric.event,
    "metricType": primary_metric.metric_type,
    "metricAgg": primary_metric.metric_agg,
    "expectedDirection": primary_metric.expected_direction,
    "metricDescription": primary_metric.rationale,
    "guardrails": guardrails_json,
})
MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "lastAction": "Metrics defined",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="measuring")

When the user asks what next: mark Measuring satisfied only after event contract and instrumentation are ready, then continue to run setup or analysis.

CF-05 / CF-06: Manage Experiment

Use when instrumentation is ready and the user wants to create, collect, analyze, refresh, close, run a bandit, or plan holdout.

Read:

• references/workspace-experiment-folder-spec.md
• references/workspace-data-source-guide.md
• references/workspace-analysis-bayesian.md
• references/workspace-analysis-bayesian-usage-patterns.md
• references/workspace-analysis-bayesian-decision-guide.md
• references/workspace-analysis-bandit.md for bandit mode
• references/workspace-analysis-holdout.md for long-term holdouts

Rules:

• Redirect upstream if hypothesis or primary metric is missing.
• If feature-flag MCP tools are available, read the bound flag before run setup and use the actual variation values for controlVariant and treatmentVariant; do not rely on manual text when the API can provide the source of truth.
• Configure experiment traffic through featbit_experiment_update_run_traffic, not the generic run update tool. The feature flag's actual served variation is the source of truth; layer eligibility only decides whether an exposure can enter this run, and analysis sampling is applied inside each served variation.
• For gradual rollouts, do not force a 50/50 flag split just to make analysis equal. Use the observed served-variation counts in the run window to choose per-variation include rates. If the flag split has changed, do not assume historical exposure events match the current flag configuration.
• When starting or resuming collection, verify the bound flag is enabled. If isEnabled is false and featbit_experiment_toggle_feature_flag is available, enable it, read the flag back, and set observationStart no earlier than the enable time. If the toggle tool is missing, stop and ask the user to register the latest MCP tools instead of pretending collection has started.
• Starting collection by enabling a flag still requires explicit user approval. Do not set confirmedByUser: true unless the user has approved that toggle in this turn or an immediately preceding approval response.
• Resume an existing run for the same hypothesis instead of creating duplicates.
• The database record is the experiment. No local sync scripts are needed.
• Analysis must run through featbit_experiment_analyze_run; do not inline-compute and write analysisResult.
• Validate inputData after analysis: k <= n, variants match, no zero n.
• If SRM p < 0.01, stop before evidence interpretation.
• Valid run statuses are only draft, collecting, analyzing, decided, archived.

Start a run:

state = MCP("featbit_experiment_create_run", experimentId=experiment_id)
run_id = newest_run_id(state)
flag = MCP("featbit_experiment_get_feature_flag", experimentId=experiment_id, key=flag_key)
if not flag.isEnabled:
    require_explicit_user_approval("enable feature flag before collection", {"key": flag_key, "isEnabled": True})
    MCP("featbit_experiment_toggle_feature_flag", experimentId=experiment_id, key=flag_key, request={
        "confirmedByUser": True,
        "isEnabled": True,
        "comment": "Enable flag before starting experiment collection",
    })
    flag = MCP("featbit_experiment_get_feature_flag", experimentId=experiment_id, key=flag_key)
    assert flag.isEnabled, "feature flag must be enabled before the run can collect data"
MCP("featbit_experiment_update_run_traffic", experimentId=experiment_id, runId=run_id, request={
    "method": "bayesian_ab",
    "controlVariant": control,
    "treatmentVariant": treatment,
    "assignmentUnitSelector": "user.keyId",
    "layerKey": layer_key_or_null,
    "layerTrafficPercent": layer_traffic_percent_or_100,
    "analysisSamplingPlan": json.dumps([
        {"variation": control, "role": "control", "includeRate": control_include_rate},
        {"variation": treatment, "role": "treatment", "includeRate": treatment_include_rate},
    ]),
    "audienceFilters": audience_filters_json_or_null,
})
MCP("featbit_experiment_update_run", experimentId=experiment_id, runId=run_id, update={
    "slug": slug,
    "status": "collecting",
    "hypothesis": hypothesis,
    "primaryMetricEvent": primary_metric_event,
    "primaryMetricType": primary_metric_type,
    "primaryMetricAgg": primary_metric_agg,
    "guardrailEvents": guardrail_csv,
    "minimumSample": minimum_sample,
    "priorProper": prior_proper,
    "priorMean": prior_mean,
    "priorStddev": prior_stddev,
    "observationStart": observation_start,
})
MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "lastAction": f"Created experiment {slug}",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="measuring")

Run or refresh analysis:

MCP("featbit_experiment_analyze_run", experimentId=experiment_id, runId=run_id, forceFresh=True)

Expert mode with inputData uses the stored pasted data automatically when live stats are not available. Do not ask the user to paste data again.

CF-06 / CF-07: Analyze Evidence

Use when data exists and a decision is being considered.

Read:

• references/evidence-decision-framing-guide.md
• references/evidence-tool-featbit-abtesting.md when the user asks for FeatBit dashboard interpretation.

Sufficiency checks before deciding:

• Both variants measured over the same time window.
• Sample per variant is at least minimumSample.
• Posterior risk is not still wide (risk[trt] and risk[ctrl] both high).
• No known external contamination.
• Instrumentation is verified for both variants.
• SRM check passes.

Decision categories:

• CONTINUE: primary P(win) >= 95%, treatment risk is low, guardrails acceptable.
• PAUSE: primary P(win) is 80-95%, possible guardrail harm, SRM/instrumentation concern, or named risk needs investigation.
• ROLLBACK: guardrail crosses strong harm threshold or primary P(win) <= 5%.
• INCONCLUSIVE: sample is below floor, risk has not converged, or primary remains uncertain after the window.

Respect analyzer output:

• Do not quote metrics that do not exist.
• Do not override verdict, p_harm, or inverse-handled outputs silently.
• If a guardrail direction looks misconfigured, ask the user to confirm and re-run after configuration changes.
• Persist the decision first. If the user asks the agent to execute the rollout decision and feature-flag MCP tools are available, read the latest flag revision and call featbit_experiment_update_feature_flag_targeting to expand, hold, or rollback the rollout. Use featbit_experiment_toggle_feature_flag to enable a launch/expansion when the flag is off, or to disable exposure for an immediate pause/rollback. Use change-request mode only when reviewer ids are supplied or approval is required.

Persist:

MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "lastAction": f"Decision: {category}",
})
MCP("featbit_experiment_update_run", experimentId=experiment_id, runId=run_id, update={
    "status": "decided",
    "decision": category,
    "decisionSummary": summary,
    "decisionReason": reason,
})

Stage stays measuring; it advances to learning only after CF-08.

CF-08: Capture Learning

Use when a decision exists or the cycle is ending.

Read references/learning-iteration-synthesis-template.md.

A complete learning contains:

1. What changed
2. What happened
3. Confirmed or refuted
4. Why it likely happened
5. Next hypothesis

Rules:

• INCONCLUSIVE cycles still produce learnings.
• Do not turn learning into a postmortem; it must feed the next iteration.
• Do not set experiment status to completed, finished, or any invented value.

Persist:

MCP("featbit_experiment_update_experiment", experimentId=experiment_id, update={
    "lastLearning": learning.summary,
    "lastAction": "Learning captured",
})
MCP("featbit_experiment_set_stage", experimentId=experiment_id, stage="learning")
MCP("featbit_experiment_update_run", experimentId=experiment_id, runId=run_id, update={
    "status": "archived",
    "whatChanged": learning.what_changed,
    "whatHappened": learning.what_happened,
    "confirmedOrRefuted": learning.confirmed_or_refuted,
    "whyItHappened": learning.why,
    "nextHypothesis": learning.next_hypothesis,
})

Then route the next cycle back to CF-01 / CF-02 inside this same skill.

Deprecated Project Sync

The old project-sync -> sync.ts -> web API -> database bridge is retired. New flows use:

featbit-experimentation -> configured FeatBit experimentation MCP server -> FeatBit API -> database

Do not call sync.ts, do not set SYNC_API_URL, and do not require a per-experiment access token in the slash command.