資料には具体的なスキルの内容や手順が記載されていないため、用途や得られる結果を特定できません。
- 用途を確認したいとき
- 入力や手順を確認したいとき
- 期待する結果を確認したいとき
法人向けの新規営業メールやLinkedInの文面、見込み客ごとの書き出し、返信案を作り、反応結果から改善点を整理します。
Cold outreach a person sends to business buyers: email and LinkedIn sequences, a first line per prospect, reply and objection answers, and a read of replies. Use when the marketer says "write a cold email sequence", "what do I send to this list", "write LinkedIn messages for these leads", "how do I reply to this prospect" or "handle this objection", "reply to this demo request", "win back or reconnect with accounts that churned or went quiet", or "why is nobody replying to my outreach". Drafts only; the marketer sends from their own tool. Not for finding the people — use research-audience; not for emails to subscribers or users, or consumer win-back — use email-lifecycle.
実行スコア 未検証
インストール方法を見る法人向けの新規営業で使うメールやLinkedInの文面を、見込み客のリストや顧客層ごとに作成します。各相手について、根拠のある情報をもとにした書き出しを用意し、複数回の連絡手順、送信間隔、各回で伝える内容を組み立てます。反論や返信への回答案、送信ツールに取り込めるCSVも作成できます。実際の返信や送信結果が共有された後は、反応率を相手の層や文面ごとに整理し、対象者、時期、メッセージなどの改善点をまとめます。見込み客リストがない場合も、保存済みの顧客層に向けた文案を作り、リスト作成など次の手順を案内します。
法人営業の初回メールやLinkedIn連絡を準備したいとき、見込み客ごとの書き出しを整えたいときに向いています。商談依頼への返信、断りへの対応、休眠・解約した法人顧客への再連絡や、反応が少ない理由の振り返りにも使えます。
このスキルは文案と分析を作成するもので、送信は行いません。見込み客の発見は research-audience、購読者や消費者向けメールは email-lifecycle の対象です。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Turns a list, or a buyer group, into words a founder or seller can send this week: sequences per segment, a first line per person, replies and objection answers, and later what the replies say to fix. The craft is in the references; this file is the order of work.
The rule that matters most: write from evidence, send nothing. Every first line rests on a dated fact about that person or account; every proof line is signed off; every message has one ask. The marketer sends from their own mailbox and tool, after warming it up — GooseWorks' own inbox is not a cold-email sender.
write-report skill. Outreach adds: the first move is who to send to first (with no list, that there is no one yet); decisions cover proof lines to sign off, the sender, the sending tool, daily volume and timing; with no list or on a first pack, the whole path and who does each step (references/handoff-and-compliance.md § The whole path); per segment the angle, the full sequence once (email and LinkedIn, with days) and 3 sample first lines; replies and objection answers; the before-you-send checklist with the one-line country summary; Sources say where each first line's facts came from and which proof was signed off and which cut. Every row's first line and finished messages go in the send file (and an appendix table when the marketer has no tool).Read research/market/positioning-<product>.md when present, else research/market/positioning.md, and its dated report. Carry the owner-selected launch direction into this work even when the live site/kit still uses earlier messaging. Unselected options stay proposals and every factual claim keeps its existing proof/approval status.
marketing-guide skill §1–2 (connect, brand, brain read) before anything else; the job line is "I write your outreach from what's saved about your brand." (get_started with wants: "more"; tool groups brand, files.)[outreach …] and [outreach-copy …] one), 22 and 23 (what we've run, what is working) and the products chapter; gooseworks:brand_knowledge_search for outreach lessons, case studies, customer results and signed-off claims. Fallback: gooseworks:brand_read (summary, kit, products with prices, learnings) and gooseworks:knowledge_search for the same. Claims: the read step of the claims-gate skill. Workspace: research/audience/ — the newest <D>-<list>/list.csv and its report.md (the segments' angles), catch-all.csv, fit-definition.md, buyer-groups.md, voc-bank.md, ledger.csv, lessons/; outreach/ — earlier packs and results reads. The sender's voice card if one exists (the voice-card skill, read only).
2b. Since last time. (In results mode, after the paste is written to the ledger — references/run-memory.md.) The Start steps of the run-memory skill, job outreach, subject = the pack (references/run-memory.md): the last record for this pack (or, for a new pack, the brand's last outreach records), last run's recommendations checked against the ledger and the brain, and reply and meeting rates per segment and per opener recounted from the ledger — 3–5 lines that open the reply and the pack or read, and that this run acts on.write-outreach-reply-reader skill for that one reply, then the draft answer.write-outreach-reply-reader skill for the batch, then references/reading-results.md.consent_basis: asked-us from research-audience's score-a-list — demo requests, trial sign-ups at companies): a warm, prompt reply that names what they did and answers it, the ask matched to urgency (now: two times this week; today: a walkthrough of one feature; 24 hours: a useful resource) — never a cold sequence.list.csv, no rows pasted) — whether or not buyer groups exist: still write the sequences for the saved buyer groups (lead first) or, with none, groups drafted from the site and brain, labelled provisional; each segment gets its default opener. The closing opens with the no-list shape (step 8). Never stop to ask for a list.Segments. Pick the row in references/outreach-by-company-type.md for the motion. Take the list's segments and angles from segments.csv next to list.csv (or the list's report.md), or the buyer groups. At most four segments in one pack; more → the top four by tier and value, the rest named as next. Each segment's angle: pain in the buyers' own words (from the quote bank, linked — no bank yet: two or three free searches for real posts, else our words labelled "not yet from buyers"), proof — one signed-off claim verbatim, or "proof needed: <what>", ask — one low-friction next step (references/email-craft.md § The ask). Timing check against the buyer's calendar: if it would land in their busiest weeks (DTC and retail from mid-November to Cyber Monday; accountants in tax season), make it the first decision: LinkedIn now with a January angle, or hold the emails.
Sequence shape per segment from where the lead came from and its signal (references/sequences.md § Lead source → sequence): touches, channel per touch (email, LinkedIn connection, LinkedIn message), days between, and what each touch adds (never "just bumping this").
Write. One worker per segment where the host supports it — given the write-outreach-draft-sequence skill pasted whole plus the segment's angle, rows and rules; at most four, one level, no web research beyond re-checking a hook's link. Otherwise segment by segment. Email rules: references/email-craft.md; LinkedIn: references/linkedin-craft.md; first line per signal: references/angles-by-signal.md.
First lines per row. For every row in the list: one first line built on its signal and hook (the fact, why it matters to their pain), or — no hook — the segment's default opener, marked "no personal hook". Never invent a fact, a compliment or a mutual connection.
Replies and objections. Per segment: the 6–8 replies the angle is most likely to draw and the answer to each (references/replies-and-objections.md) — positive, "send info", timing, "we use X", price, wrong person (ask for the referral), not interested, unsubscribe.
Check with the write-outreach-outreach-qa skill: every proof line through claims-gate (cuts listed with the proof that would restore them), country rules per row (references/handoff-and-compliance.md § Country rules), length and character limits, one ask, no tracking-dependent advice, no banned hooks. Every price or offer in a message follows the guide's rule "Live facts" (the marketing-guide skill §3); a mismatch with the brain is flagged in decisions. Fix, then re-check.
Deliver through write-report: the pack as a local file with a clickable link, the send file beside it, a copy saved with the save-to-brain skill (the pack's brand copy keeps the angles, sequences and replies but no person's name, company or first line — per-person text lives only in the local file and the send file) and a gooseworks:file_url link. Write each row's opener_id and sequence_id into the ledger (columns only; references/run-memory.md), then run-memory's End steps: the run record with the counts and the "send first" call as a recommendation with its test.
One closing message, this shape (with no list, the first line is replaced by the no-list block below — never "Send first: <segment, N a day>" without people):
Send first: <segment, how many people, which touch> — <why>. <pack title> · send file: <name> · saved to your brand: open Decisions: <proof lines to sign off> · <sender and daily volume, if assumed> · paste the replies here as they come in — I'll draft answers and tell you what to fix.
No list:
Before you can send: you don't have anyone to send these to yet. Two ways on: (a) Say build the list — I'll find a first batch of about 50 right-fit companies and the buyer at the best of them (name, title, LinkedIn). A test you can read needs 300–500 accounts, so we build it in batches. I can't find or check email addresses yet, so you'll run them through an email finder (such as Apollo or Hunter) and a checker (such as ZeroBounce or NeverBounce) before sending. Start setting up a separate sending domain today — it needs 2–4 weeks of warm-up. (b) Already have a list? Paste or upload it and I'll write a first line for every person. then the links line and Decisions as above. (When the connector can find and check addresses, (a) says so instead.) The run record's recommendation is "build or paste the list".
No schedule. A pack is a one-off job. Reading results is done when the marketer pastes them (suggest doing it after the first 100 sends or two weeks, whichever is first); that read is the same run-memory job and subject, so it starts from this pack's record.
write-outreach-reply-reader skill), draft the answer in the sender's voice with one ask, and say the status it gives the row (outcomes-ledger's person statuses: replied, meeting, not-a-fit, bounced, unsubscribed, do-not-contact). Unsubscribe or "stop" → no reply draft beyond a one-line confirmation if their tool needs one, and the row is suppressed.list_id = the paste's date) so their replies can be matched and suppressed. Counts only → the closing decisions ask who is behind each stop, angry reply, bounce and not-interested, with a Hold needed line meanwhile (references/reading-results.md § Counts only). reply-reader for every reply; then references/reading-results.md — rates by touch and segment, where objections cluster, what to fix (targeting, titles, timing, message, deliverability), with sample sizes said. Update the ledger and write lessons through the outcomes-ledger skill (entity: person). A rule the marketer states goes through the save-learning skill as [outreach-copy YYYY-MM-DD]. Deliver the read through write-report, opening with "since last time", then run-memory's End steps (the record's numbers per segment and per opener, the fixes as recommendations with review-by and test).| Artifact | Where | Read by |
|---|---|---|
| Outreach pack | local gooseworks-reports/<brand-slug>/<D>-outreach.md; workspace outreach/<D>-<slug>/pack.md → Outreach pack — <slug> — <Brand> — <D> (keep-history) | the marketer, the next run |
| Send file | local gooseworks-reports/<brand-slug>/<D>-outreach-send.csv; workspace outreach/<D>-<slug>/send.csv (not imported — it holds people's data) | the marketer's sending tool |
| Results read | local gooseworks-reports/<brand-slug>/<D>-outreach-results.md; workspace outreach/<D>-<slug>/results.md → Outreach results — <slug> — <Brand> — <D> (keep-history; counts, rates and fixes only — no names, emails, reply text or per-reply rows); per-reply lines and the Hold / Find / Quote request lines in outreach/<D>-<slug>/results-working.md (not imported) | the next pack, research-audience (re-scoring signals) |
| Run records | runs/write-outreach/outreach/<pack-slug>/<D>.md → Run record — outreach — <pack-slug> — <Brand> — <D> (keep-history, via run-memory; counts, rates and links only) | the next read or pack |
| Ledger statuses, lessons | research/audience/ledger.csv, research/audience/lessons/<D>.md (outcomes-ledger, entity: person) | research-audience, the next pack |
"Since last time" opened the reply (or "no earlier run"), rates per segment and per opener recounted from the ledger with sample sizes, and the run record saved with counts and links only. The pack passes write-report §3 and outreach-qa. Per segment: an angle in the buyers' words with a link, signed-off proof or "proof needed", one ask; a sequence of email and LinkedIn touches with days and what each adds; within the character limits; 6–8 replies and objection answers. Every list row has a first line resting on a dated fact, or says "no personal hook". Every row's country rule is applied (footer and opt-out on email; no email for rows that can't take one). The send file imports (one subject and body column per touch). With no list, the closing opened with the no-list block and named no send volume. Nothing was sent. In results mode: replies classified, rates by touch and segment with sample sizes, what to fix, ledger updated.
Finding people and emails — research-audience · emails to subscribers and customers, consumer win-back — email-lifecycle · creators — work-with-creators · sending, connecting, scheduling, warming up domains — not covered (the marketer's tool) · SMS, cold calls · scraping who liked or commented on posts · personal emails or phones · sales decks, battlecards — not covered yet.
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資料には具体的なスキルの内容や手順が記載されていないため、用途や得られる結果を特定できません。
顧客の課題や商品情報、広告事例を調べ、根拠のある広告訴求と静止画・動画・コピー案にまとめます。動画案は実際の制作フォーマットとの相性も確認し、商品単位の角度集として整理します。
Google、Meta、LinkedInなどの広告実績を分析し、成果の出た広告や予算の無駄を特定します。停止・拡大・テスト案や、具体的な予算配分を提案します。
広告費と登録後の成果を照合し、質の高いリード獲得にかかった実コストを算出します。広告素材ごとに拡大・維持・調査・停止の判断材料を整理するスキルです。
Meta広告を始める前にブランド情報を調べ、予算や広告表現に関わる不明点だけを質問して準備を整えます。確認事項と仮定を記録し、ブランド情報とキャンペーン概要を作成します。
広告文や画像内の表現を配信先と商品カテゴリのルールに照らして確認し、直せる箇所を修正しながら、法務や人の判断が必要なリスクを広告ごとに整理します。