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Product feedback learning loop

Skill SylphxAI/skills/skills/product-feedback-learning-loop

Design or audit universal private product feedback intake and the learning loop that ingests authorized private feedback and public reviews, preserves source evidence, clusters underlying problems, routes support and safety cases, links product actions, validates outcomes, publishes policy-safe review responses, and closes the loop truthfully. Use for apps, games, web products, services, and platforms when the requested artifact is feedback capture, review ingestion/response, qualitative evidence synthesis, product-learning operations, or customer status updates. Do not use for public rating or review request eligibility, prompt timing, native solicitation surfaces, or cooldowns; compose review-solicitation-policy only when public solicitation is also requested.From its SKILL.md

Install
npx -y skills add SylphxAI/skills --skill product-feedback-learning-loop

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 26 days oldThe repository was created 26 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

6.8 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Product Feedback Learning Loop

Turn authorized feedback and review signals into source-preserving evidence, safe routing, validated product action, and truthful customer updates. Do not turn this learning surface into a public-review funnel.

Resource guide

Read references/feedback-learning-loop.md for intake, consent, normalized signals, taxonomy, dedupe, evidence quality, routing, authorized public-review ingestion and response, product action, metrics, and privacy tests. Retrieve current provider/API authority before ingesting or responding on an external platform.

Composition contract

Begin a composed artifact with the product artifact envelope. Set ownerSkill: product-feedback-learning-loop and give the feedback/learning artifact its own artifactId, artifactVersion, artifactRevision, and artifactState. The top-level artifact never self-hashes.

Every typed input names the exact producer contract through fulfillsHandoffId. A draft input carries identity/revision/state but no digest; a sealed input additionally requires artifactDigest and digestRule: sha256-exact-bytes. Never invent a digest or resolve a moving “latest” alias.

For a combined private-feedback/public-review request, produce two sibling artifacts with distinct identities and stable producer-owned handoffIds. They may share exact upstream product and value-event inputs. Add a one-way input edge only when this loop truly consumes a contract emitted by Review Solicitation; public request eligibility can never consume private sentiment or learning state, and the graph must remain acyclic.

Workflow

  1. Record the product scope, feedback and review sources, entry contexts, audiences, privacy/retention needs, support/safety owners, product decision owners, current external authority, and evidence gaps.
  2. Define universal private intake with optional free text, minimal structured context, accessible entry points, attachment/diagnostic preview and consent, anonymity or account-link choice where feasible, contact permission, acknowledgement, offline retry/dedupe, retention, access, and deletion.
  3. Ingest authorized feedback and public reviews without flattening provenance. Preserve source, locale, release, context, original evidence, selection bias, redaction, consent, identity confidence, and correction/deletion state.
  4. Classify and dedupe by the underlying user problem and affected state, not exact wording, star value, payer value, loudness, or the user's proposed solution. Preserve contradictory and minority cohorts.
  5. Route security, privacy, safety, abuse, crash, data loss, payment, entitlement, accessibility, refund, and urgent support signals immediately to their owners. A learning queue cannot delay incident or customer remedy.
  6. Link each material cluster to prevalence denominators, severity, mechanism hypotheses, contradictory evidence, owner, proposed product/support/content/ instrumentation action, independent validation, rollout, live outcome, and durable decision evidence.
  7. Respond publicly only through a current authorized route and with verified, privacy-safe facts. Close private loops as received, clarifying, investigating, fixed, shipped, not planned, policy-limited, or support-resolved. Never promise an uncommitted feature or date.
  8. For a request that also asks when or how to solicit public reviews, invoke review-solicitation-policy as a sibling. Return two independent, versioned artifacts with stable handoff IDs; never use feedback or inferred sentiment to gate its public request policy.

When not to use

  • For generative interviews, observation, contextual inquiry, concept evaluation, or usability discovery before or beyond recurring feedback operations, use user-research-and-discovery.
  • For public review request eligibility, prompt timing, native solicitation, cooldown, or platform request policy, use review-solicitation-policy.
  • For end-to-end support, refund, incident, analytics implementation, listing conversion, or store submission, route to the corresponding specialist and keep this skill limited to evidence-preserving product learning.

Boundaries

  • social-media-operations-review owns recurring official-account publishing, platform readback, listening, reply routing, crisis, impersonation, rights, recovery, and shutdown; this skill owns authorized feedback/review ingestion, public response evidence, product action, and close-loop.
  • customer-support-case-resolution owns one private customer's facts, remedy, reply, protected-action handoff, verification, and closure. Link the case without exposing private facts in a public response.
  • Do not own public review eligibility, prompt timing, native request surfaces, cooldowns, or platform solicitation policy.
  • Never route happy users to public review and unhappy users to private feedback. Private feedback/help is universal and independent.
  • Do not let stars, volume, payer value, model confidence, or praise dictate roadmap priority. Do not trade safety, law, accessibility, or trust through a universal score.
  • Do not reuse praise as an endorsement without permission and disclosure, deanonymize reviewers, retaliate, reveal account data, argue publicly, ask for a higher rating, or publish unsupported fixes.
  • The classifier or proposer cannot be its sole validator or promoter.
  • Do not rebuild whole support operations, refunds, analytics implementation, incident command, listing conversion, or store submission.

Output

Artifact envelope, exact inputs, proof state, stable handoff outputs, and assumptions:

Scope, sources, authority, and evidence gaps:

Private feedback contract:

Normalized signal, taxonomy, and evidence-cluster contract:

Urgent support/safety routing:

Authorized review ingestion and response policy:

Product action, validation, rollout, and close-loop state:

Sibling handoffs:

Validation, unresolved authority, and next proof:

Keep looking

Skills are one crate of 326,512. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.