ad-policy-check
Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the…
Step of the research-audience playbook: scores and tiers list rows, groups them into segments with an angle each, runs the pre-handover checks and writes the list files. Run research-audience instead; not for direct use.
$ npx -y skills add gooseworks-ai/goose-skills --skill research-audience-score-segment-qa --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/research-audience-score-segment-qaContext preview
The summary Claude sees to decide when to auto-load this skill.
Step of the research-audience playbook: scores and tiers list rows, groups them into segments with an angle each, runs the pre-handover checks and writes the list files. Run research-audience instead; not for direct use.
name: research-audience-score-segment-qa description: >- Step of the research-audience playbook: scores and tiers list rows, groups them into segments with an angle each, runs the pre-handover checks and writes the list files. Run research-audience instead; not for direct use. version: 0.1.0
**Reads:** account table, people rows, signals; `../research-audience/references/fit-and-scoring.md`; `../research-audience/references/output-format.md`; `../research-audience/references/contacts-and-compliance.md`.
**Does:** 1. Score: gates already passed; add points by block, each with its cited reason in `fit_reason`. Tier A/B; C to `dropped.csv`. Set the value tier (V1/V2/V3) with its evidence for account-based lists; rank by tier and value together. 2. Segment by angle × signal (e.g. "new leader", "hiring for the pain", "fit, no signal"), 3–6 segments, each with a **three-line angle**: *pain* (the situation in the buyer's terms, from the signals), *proof* (one signed-off claim, verbatim, cleared through the `claims-gate` skill (`../claims-gate/SKILL.md`) — or "you'd need proof of <X> first", which the message offers to sign off), *ask* (one low-friction next step). Never an invented result or number. Then **For you** per segment: does it fit us (yes/no and why, from our product, pricing and customers); what to send first and to whom (V1 accounts first); then offer, as a closing decision, the messages written for this list (write-outreach reads `list.csv` and these angles). Don't follow the herd: if every rival pitches this segment the same way, say so and offer a different angle from what buyers say (this playbook's quote bank, `research/audience/voc-bank.md`). **You vs them**: accounts where a rival is in use (from job posts naming the tool, case studies, integration pages) — where we're ahead (say it louder), behind, and where everyone sounds the same. 3. Rank: tier, then score, then warm path. 4. QA checklist — every item, every run:
5. If fewer rows pass than asked, deliver what passed and state why (thin universe, no contact routes, heavy exclusions). Never pad.
**Writes:** `report.md`, `list.csv`, `dropped.csv` (tier C included, with reason) in the output format, and **`segments.csv`** (`segment, name, pain, pain_source_url, proof, ask, default_opener, rows`) — the angles write-outreach reads; pain comes from the quote bank, or with no bank from the rows' own signals ("hiring two AP specialists"), labelled so. Ledger rows for everything shown.
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