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@c805268
by shingo imotasimota/agent-skills85 stars
15

Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.

Use this Skill: https://skilld.dev/gh/simota/agent-skills/voice

This session only. Nothing lands on disk.

referencecsat-ces-measurement.md

≈1.4k tokens on demand. Your agent reads this file only when SKILL.md points to it.

CSAT & CES Measurement

Read for operational touchpoint satisfaction/effort surveys. Voice defines the instrument; Field owns one-off research design, Pulse owns dashboard governance, and Echo produces synthetic hypotheses—not measured customer responses.

Instrument and scale contract

Freeze question wording, labels, direction, channel and instrument version before collecting responses. Never pool different scales or wording variants without a documented comparability analysis.

Instrument Question Scale Calculation
CSAT How satisfied were you with [specific interaction]? 1 Very dissatisfied; 2 Dissatisfied; 3 Neutral; 4 Satisfied; 5 Very satisfied 100 × count(score in {4,5}) / valid answered ratings. Label T2B; a T1B result is not interchangeable.
CES agreement [Company] made it easy for me to [task]. 1 Strongly disagree through 4 Neutral to 7 Strongly agree Mean of valid 1–7 ratings; higher means easier.
CES ease variant How easy was it to complete [task]? 1 Very difficult through 4 Neutral to 7 Very easy Same numeric direction, but record a distinct instrument version; do not silently mix with agreement wording.

Report valid n, invited/eligible denominator, response rate, missingness, time window, cohort/channel, distribution, T2B or mean, and the uncertainty method/interval. With n=0, report not estimable. Keep CES 4 neutral in raw data; do not recode it as a high-effort answer to strengthen a risk claim.

Touchpoint and survey rules

Touchpoint Instrument / trigger
Support resolution Select CSAT or CES for the decision; within 2h of close.
Self-service / settings / task completion CES; article use within 30min, other tasks after completion.
Onboarding / first successful feature use / checkout CSAT after completion, or CES when task effort is the explicit question.
Cancellation intent Exit survey, not a satisfaction proxy.
Relationship / advocacy NPS, not a touchpoint CSAT substitute.

One rating + free-text why + at most one optional segment question; no bundled CSAT/CES/NPS survey. Ask neutrally, not from a relationship-biased sender. Apply the existing 30-day cross-survey suppression rule. Preserve consent, minimization and access restrictions; use a protected stable subject identifier where follow-up is authorized.

Follow up on CSAT 1–2 (cause of dissatisfaction) or CES 1–3 (highest-friction step) with the recovery owner within 24h. For positive scores, ask what to preserve only when that qualitative context is needed. A missing why is missing evidence, not a guessed reason.

Operational decision defaults

These are Voice defaults to confirm against the engagement, not universal industry benchmarks or independent authorization to stop a release.

Metric Default interpretation / next action
CSAT T2B ≥85%; 75–84%; 65–74%; <65% Maintain / iterate on bottom-box themes / targeted improvement / escalate to the accountable owner.
CSAT bottom-box (1–2) Target ≤5%; >10% triggers service-recovery escalation even with high T2B.
CES mean ≥6; 5–<6; 4–<5; <4 Maintain / improve difficult tasks / investigate effort sources / escalate critical effort.
CES dashboard target Mean ≥5.5, high-effort share (1–3) <20%, low-effort share (5–7) >60%; name this target separately from the interpretation bands.

For a launch/block decision, include the sample limits, measured effect, owner and the approved release gate; Voice does not invent deployment authority. Compare external benchmarks only after matching source year, population, question, scale, scoring and channel. Otherwise report non-comparable rather than using a cached industry table.

Triangulation

Join CSAT/CES/NPS only on comparable cohorts/windows; interpretations below are investigation hypotheses, not measured causes.

CSAT / CES / NPS Investigate / action
High / High / High Protect the observed experience.
High / High / Low Brand/value or advocacy gap.
High / Low / High Loved product with task friction; investigate and reduce effort.
High / Low / Low Possible hidden dissatisfaction/churn risk; inspect verbatim evidence.
Low / High / High Specific dissatisfaction despite loyalty; recovery follow-up.
Low / Low / Any Service-recovery priority; close the loop within 24h.
Mixed cohorts Report the divergence; do not let the aggregate conceal the affected cohort.

Minimal data contracts

interface CSATResponse {
  score: 1 | 2 | 3 | 4 | 5;
  touchpoint: string;
  feedback?: string;
}
interface CESResponse {
  score: 1 | 2 | 3 | 4 | 5 | 6 | 7;
  touchpoint: string;
  feedback?: string;
  userId: string; // protected identifier; never export identity in public reports
  timestamp: string;
}

Store instrument version, collection window, eligibility/response denominators and cohort metadata alongside the response collection. Keep consent/access metadata under the project's existing privacy contract; do not infer consent from a score.

Report and handoff

Output: metric/value/target/status; touchpoint/score/n/trend; high-effort issue/count/verbatim evidence/uncertain cause; priority/current→target/action/owner; sampling limits and follow-up disposition. Do not fill root-cause cells without evidence.

Route why text to voice thematic, relationship triangulation to voice nps, stable metrics to Pulse, recovery cohorts to Growth, redesign briefs to Spark, effort hypotheses to Echo, reliability-correlated drops to Beacon, and strategic cohort trade-offs to Magi. The handoff carries instrument version, cohort/window and evidence—not an unsourced industry rank.

Source: SKILL.md on GitHub

1 warning13d5 checks · Risk SAFE
  • Gen Agent Trust Hub13d

    The skill is designed for customer feedback analysis and follows industry-standard metrics (NPS, CSAT, CES) while prioritizing data privacy through pseudonymization. It includes security-focused logic to detect synthetic (AI-generated) feedback and bot patterns. However, like any agent that processes external user-supplied text from reviews and support tickets, it possesses an inherent surface area for indirect prompt injection.

  • Socket13d

    No alerts

  • Snyk13d

    Risk: MEDIUM · 1 issue

  • Runlayer6mo

    1/6 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at c805268. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 2 weeks ago

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