customer-intel

Solid

Synthesize recent customer feedback and pain points

AI & Automation 476 stars 127 forks Updated yesterday NOASSERTION

Install

View on GitHub

Quality Score: 83/100

Stars 20%
89
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

<!-- Generated from `.claude/skills/_available/product/customer-intel/SKILL.md` by `scripts/generate-agents-skills.py`. Do not edit. --> ## Purpose Aggregate and analyze customer feedback from all sources - meeting notes, person pages, feedback captures - to identify patterns, prioritize pain points, and inform product decisions. ## Evidence, authority, and recovery Treat customer intelligence as an evidence ledger, not as memory or a polished guess. - For every finding, retain a stable source ID (vault path, note ID, or capture ID), source type, source date (when the customer said or wrote it), and the as-of date (when this review read it). Keep the source ID and source date attached when a finding is summarized. - Deduplicate repeated copies of the same evidence for frequency counts, but preserve every source ID and source date in the provenance. Distinguish a copied meeting note from an independent mention; duplicate copies are not extra customer mentions. - Preserve quote fidelity: quoted text must be copied exactly, including wording and meaningful punctuation. Mark omissions or inaudible text explicitly, and label any cleaned-up summary as a paraphrase rather than a quote. - Keep unknown and contradictory evidence visible. If sources disagree, show each claim with its source ID and date and call out the contradiction; do not silently choose one. If the records do not support a count, customer attribution, urgency, trend, or roadmap status, write `unknown` and retur...

Details

Author
davekilleen
Repository
davekilleen/Dex
Created
7 months ago
Last Updated
yesterday
Language
Python
License
NOASSERTION

Integrates with

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category

Code & Development Listed

customer-insight-synthesizer

Synthesize patterns across multiple interviews, calls, reviews, surveys, or support messages into evidence-backed needs, language, objections, and actions.

1 Updated 3 weeks ago
nahiddotai
AI & Automation Solid

audience-intel

Use when synthesizing dated customer conversations, feedback, or behavior evidence into audience or persona insight, especially when sources are numerous, repeated, time-bounded, or disagree.

476 Updated yesterday
davekilleen
AI & Automation Listed

customer-interview-synthesis

Turns a pile of customer, user or stakeholder interviews, call transcripts, survey responses and support tickets into findings that can carry a decision. Produces a coverage statement naming the gaps, a coded corpus where every tag keeps its verbatim, themes reported on both frequency and intensity, the contradictions and the absences rather than an average, five to eight findings each with counts, quotes, segment pattern and a confidence level, and implications tied to the decision the research serves. Use this skill when someone has interview notes and asks what they say, wants research synthesised, needs a findings readout, asks what customers told us, wants calls summarised, wants to move from anecdotes to evidence before a roadmap, pricing or positioning decision, or says everyone has a different view of what the customers want. Trigger for any qualitative input from more than two sources.

0 Updated 3 days ago
ingridleiria