sales-skills/sales-noisely

Noisely Platform Help

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

1. What are you trying to do?

   • A) Push action items / spike alerts to your tools (Slack, Linear, Jira, webhook…)
   • B) Pick which of the 23+ sources to monitor (reviews, social/dev communities, support)
   • C) Understand the AI output — categories, sentiment, urgency/impact, clustered action items
   • D) Export feedback to a sheet/BI, or import your own via CSV
   • E) Tune signal — brand-ambiguity filters, dedupe, spike thresholds
   • F) Decide Try ($29 one-time) vs Pro ($49/mo) vs Custom, or budget the analysis credits

2. Code or no-code? Custom routing → outbound webhook. Otherwise use the native channels (Linear/Jira/Slack/Sheets) — no endpoint to host.

Skip-ahead rule: if the user's prompt already provides enough context, skip to Step 2.

Step 2 — Route or answer directly

| If the question is about... | Route to... | |---|---| | Survey / VoC program strategy (NPS/CSAT/CES design) across tools | /sales-customer-feedback {question} | | Collecting public reviews (Trustpilot/G2 generation) strategy | /sales-customer-reviews {question} | | Broad brand/social listening across tools | /sales-social-listening {question} | | Wiring Noisely into a CRM/ticketer generically (iPaaS) | /sales-integration {question} |

When routing, give the exact command, e.g. "This is a strategy question — run: /sales-customer-feedback design a closed-loop VoC program".

Step 3 — Noisely platform reference

Read references/platform-guide.md for the full reference — the source/analysis/output model (what's UI-config vs webhook vs CSV), the credit-based pricing, the action-item data model, and quick-start recipes (route action items to Linear/webhook; export to Sheets; tune brand-ambiguity filters).

Read references/noisely-api-reference.md for the integration surface — no public REST API is documented; the surface is incoming sources (23+, UI-config), the AI pipeline (12 categories → sentiment → urgency/impact → clustered action items → spike detection), 10 outbound channels including webhooks (payload schema not published — capture a live delivery), and CSV import/export.

Answer using only the relevant section. Don't dump the full reference.

Step 4 — Actionable guidance

Focus on the user's specific situation:

• The unit of automation is the "action item," not the mention. Noisely clusters/dedupes similar feedback across sources into ranked action items. Wire downstream logic (tickets, alerts) to the action-item id, not raw mentions.
• No public REST API — use webhooks + native channels. To create tickets, push action items to Linear/Jira/Asana; for chat alerts use Slack/Teams/Discord; for custom routing use the webhook. The webhook payload schema isn't published — capture a test delivery (webhook.site) and pin your mapping to it.
• Watch the analysis credits. "AI analyses" are a monthly credit limit (≈2,000/mo on Pro). High-volume sources (Reddit/app stores) can burn credits — scope sources and use brand-ambiguity filters to cut noise.
• Brand-ambiguity filters matter for noisy names. If your brand name is also a common word, the filters reduce off-topic mentions — invest in them early or your action items will be polluted.
• CSV is the reliable structured export. Absent an API, export the dashboard to CSV for BI, or import your own support data as a source.
• Right-size the plan. Try ($29 one-time, ≤500 mentions) to validate, Pro ($49/mo, 23+ sources, 10 channels, unlimited seats) for ongoing, Custom for higher limits/SLA. Pricing best-effort — verify.

If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.

Gotchas

Best-effort from research (2026-06) — features/pricing verified against the marketing + comparison pages; no public API docs exist, so the developer surface is inferred from documented channels. Confirm in-account.

1. No public REST API. The docs/webhooks path 404s; automation is outbound webhooks + native integrations + CSV. Don't assume an api.noise.ly exists — confirm with Noisely (Custom plan mentions custom integrations).
2. Webhook payload isn't documented. Event names and fields are unpublished — capture a live delivery before coding, and there's no documented HMAC (secret URL + dedupe on action-item id).
3. Credits are the real limit. AI analyses are capped monthly (~2,000 on Pro); noisy sources eat them fast — scope sources, not just plans.
4. Brand ambiguity pollutes results. Common-word brand names pull off-topic mentions until you train the ambiguity filters.
5. It's aggregation, not surveys. Noisely mines existing feedback (reviews/social/support) — it does not run NPS/CSAT survey programs. For survey strategy use /sales-customer-feedback.
6. Overlaps social listening & reviews. It ingests Reddit/HN and Trustpilot/G2 — but it's framed for product feedback intelligence, not broad PR monitoring or review generation. Route those to /sales-social-listening / /sales-customer-reviews.
7. Source coverage shifts. The 23+ source list changes; verify a specific source (e.g. a niche forum) is supported before relying on it.

Related skills

• /sales-customer-feedback — Voice-of-customer / survey program strategy across tools (Noisely is the aggregation/intelligence option vs survey tools) — program design, tool selection
• /sales-customer-reviews — Public review collection/generation strategy (Noisely reads reviews; this is about getting more)
• /sales-social-listening — Brand/social monitoring across tools (Noisely overlaps on Reddit/HN/social but is product-feedback-framed)
• /sales-integration — Wiring Noisely action items into a CRM/ticketer via webhooks/native channels
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do -a claude-code

Examples

Example 1: Route high-urgency action items into Linear via webhook (developer/automation)

User says: "When Noisely flags an urgent bug cluster, I want it to open a Linear issue automatically." Skill does: Recommends the native Linear integration first (turn an action item into an issue, no code). If custom logic is needed (e.g. only urgency=high + specific category), use the webhook outbound channel → your handler → Linear API. Warns the payload schema isn't documented (capture a live delivery, key on the action-item id), there's no HMAC (secret URL + dedupe), and that high-volume sources burn analysis credits. Result: Urgent clusters become Linear issues without manual triage.

Example 2: Which sources should I turn on, and will I blow my credits?

User says: "I sell a mobile app. What should I monitor in Noisely without wasting analyses?" Skill does: Suggests high-signal sources for an app (App Store, Play Store, Reddit, support via Zendesk/Intercom, G2/Trustpilot) and warns that broad sources (Reddit/Google News) can burn the ~2,000/mo Pro credit fast. Recommends tuning brand-ambiguity filters and starting on Try ($29) to validate volume before committing. Routes survey-based VoC to /sales-customer-feedback. Result: A focused source set that fits the credit budget.

Example 3: Noisely vs a survey tool — which do I need?

User says: "Is Noisely a replacement for our NPS surveys?" Skill does: Clarifies Noisely aggregates existing feedback (reviews, social, support) into AI action items — it's not a survey platform and won't run NPS/CSAT. They're complementary: surveys = solicited/structured, Noisely = unsolicited/at-scale. Routes program design: "run: /sales-customer-feedback design an NPS + VoC program," and notes Noisely's edge is affordable cross-source mining ($49/mo vs enterprise tools). Result: User understands the categories and picks the right mix.

Troubleshooting

My action items are full of off-topic mentions

Symptom: Clusters include posts that aren't about your product. Cause: Your brand name is ambiguous (also a common word/another brand), so the source crawlers pull unrelated mentions. Solution: Train the brand-ambiguity filters (Noisely learns to exclude off-topic mentions), narrow the source set, and remove the noisiest sources. Re-run analysis after tuning. This also conserves AI analysis credits, which off-topic mentions waste.

I want to pull Noisely data into our data warehouse / app but can't find an API

Symptom: Looking for a REST endpoint to read action items/mentions. Cause: Noisely doesn't publish a public REST API. Solution: Use the webhook outbound channel to stream action items to your endpoint (capture the payload shape from a live delivery; key on the action-item id), the Google Sheets channel for BI, or CSV export from the dashboard for batch. For higher-volume/custom needs, ask Noisely about Custom-plan custom integrations. For generic iPaaS wiring, use /sales-integration.

I burned through my monthly analyses

Symptom: AI analyses hit the cap before month-end. Cause: Credits (~2,000/mo on Pro) are consumed per analyzed mention; high-volume sources exhaust them. Solution: Reduce/disable the noisiest sources, tighten brand-ambiguity filters to drop off-topic mentions before analysis, and consider the Custom plan for higher limits. Prioritize sources where your buyers actually post.