# Writing Outreach Skill Use this skill when the task involves cold emails, personalization, lead scoring, qualification, sequence design, campaign copy, or inspection of enrichment results. ## What This Skill Does - Loads ICP/criteria context from a local file (for example `./icp.md`) using `read_file`. - Produces strict, structured qualification output: - numeric score (`0-10`) - fit label (for example `Strong fit: 8/10`) - rationale summary - question-by-question yes/no/unknown answers with confidence and rationale - Produces strict, structured 4-step outbound email sequence: - subject - core value prop - message body - sequence-level rationale ## Required Inputs - `prospect_payload`: Person + company context (JSON string/object in row) - `icp.md` (or equivalent): Local qualification criteria and positioning constraints ## General Setup (Recommended) Create a small context pack in your working directory before running this flow: - `icp.md`: Ideal customer profile and disqualifiers - `qualification_questions.md`: The exact questions you want scored - `product_context.md`: Value props, proof points, and constraints - `copy_rules.md`: Tone, banned claims, length limits, CTA style Minimal folder layout: ```text ./context/ icp.md qualification_questions.md product_context.md copy_rules.md ``` Example `icp.md` starter: ```markdown # ICP ## Best-fit companies - B2B SaaS, 200-5000 employees, multi-product GTM team - Uses modern data stack and CRM-based workflows ## Best-fit personas - VP/Head/Director in Marketing, RevOps, Sales Ops, GTM Ops - Owns pipeline quality, segmentation, scoring, or campaign orchestration ## Core pains - Slow GTM iteration due to analyst/engineering dependency - Low trust in black-box scoring - Weak signal-to-action workflow for reps ## Disqualifiers - <20 employees - Pure B2C motion - No clear sales/marketing operations function ``` ## Best Practices - Keep `icp.md` specific and opinionated. Broad ICPs create generic copy. - Separate facts vs assumptions in your context docs. - Put proof points in `product_context.md` so emails can reference them cleanly. - Define hard copy constraints in `copy_rules.md`: tone, max words, banned phrases, allowed CTA types. - Use `Unknown` for missing evidence in qualification instead of guessing. - Run the QA prompt template before finalizing sequence copy. - Version your context docs with the campaign (`context-q1-enterprise.md`, etc.). ## Output Contracts ### Qualification JSON Contract ```json { "data": { "score": 8, "score_label": "Strong fit: 8/10", "fit_band": "STRONG_FIT", "rationale": "Short summary of fit and caveats.", "qualification": { "answers": [ { "question": "string", "answer": "Yes", "confidence": "HIGH", "rationale": "string" } ], "summary": { "positives": ["string"], "risks": ["string"], "next_checks": ["string"] } } } } ``` ### Email Sequence JSON Contract ```json { "data": { "emails": [ { "step": 1, "subject": "string", "coreValueProp": "string", "email": "string" } ], "sequence_rationale": "string" } } ``` ## Personalization: `run_javascript` vs `deeplineagent` When you already have contact + company context in CSV columns, use `run_javascript` for email generation when a deterministic template is enough. Use `deeplineagent` when you want AI help with reasoning, scoring, copy quality, or research. **Critical: avoid mail-merge output.** If every email has the same structure with only `{{first_name}}` and `{{company_name}}` swapped, it's a template — not personalized outreach. Each email must reference something specific to the company (product, use case, industry, recent news). Use `company_description`, `one_liner`, `company_research`, or other enrichment columns in your JS template or `deeplineagent` prompt so each email is substantively different. Author the generation column(s) as a custom play over your enriched CSV (see `deepline plays --help`) with one `withColumn` per output: - Fast path: a `run_javascript` column applying a deterministic template. - AI path: a `deeplineagent` column with the research columns interpolated into the prompt and a `jsonSchema` of `{subject, email}`. - Research-first path: a research `deeplineagent` column, then the generation column consuming it. Notes: - `{{company_research}}` is the safe form for AI prompts when the prior column came from `deeplineagent`. - If you need a single field from prior research for deterministic template logic, extract it into its own scalar column with `run_javascript` first, then reference that scalar column in later steps. - Inside `run_javascript`, use `row["company_research"]` or `row.company_research`. Do not use `{{row.company_research}}`; `row` is only available inside the JS code. ## Recommended Workflow ```bash deepline describe deeplineagent ICP_CONTEXT=$(cat ./context/icp.md) PRODUCT_CONTEXT=$(cat ./context/product_context.md) QUAL_WITH=$(jq -nc --arg icp "$ICP_CONTEXT" '{ alias: "qualification_output", tool: "deeplineagent", payload: { model: "openai/gpt-5.4-mini", prompt: ("You are a B2B qualification analyst. Use only the provided evidence. Inputs:\nICP context:\n" + $icp + "\nProspect payload:\n{{prospect_payload}}"), jsonSchema: { type: "object", properties: { data: { type: "object", properties: { score: { type: "number" }, score_label: { type: "string" }, fit_band: { type: "string" }, rationale: { type: "string" } }, required: ["score", "score_label", "fit_band", "rationale"], additionalProperties: false } }, required: ["data"], additionalProperties: false } } }') SEQ_WITH=$(jq -nc --arg product "$PRODUCT_CONTEXT" '{ alias: "email_sequence_output", tool: "deeplineagent", payload: { model: "openai/gpt-5.4-mini", prompt: ("You are a B2B email strategist. Write 4 concise emails tied to the qualification output and product context.\nProduct context:\n" + $product + "\nQualification payload:\n{{qualification_output}}\nProspect payload:\n{{prospect_payload}}"), jsonSchema: { type: "object", properties: { data: { type: "object", properties: { emails: { type: "array", items: { type: "object", properties: { step: { type: "number" }, subject: { type: "string" }, coreValueProp: { type: "string" }, email: { type: "string" } }, required: ["step", "subject", "coreValueProp", "email"], additionalProperties: false } }, sequence_rationale: { type: "string" } }, required: ["emails", "sequence_rationale"], additionalProperties: false } }, required: ["data"], additionalProperties: false } } }') printf "prospect_payload\n{\"person\":{\"firstName\":\"Rachael\",\"lastName\":\"Foster\",\"title\":\"Vice President AMER Field Marketing, Public Sector, Services, & Community\"},\"company\":{\"name\":\"Cloudera\",\"domain\":\"cloudera.com\"}}\n" > ./qualification_email_seed.csv # Run both columns as a custom play over the seed CSV (one withColumn per # payload above) — `deepline plays --help` covers authoring. ``` ## Prompt Templates (Copy/Paste) Use these as `prompt` values inside `deeplineagent` payloads. Pair each prompt with a `jsonSchema` that matches the output contracts above. Recommended starting model: `openai/gpt-5.4-mini`. ### 1) Score + Fit Label Template ```json { "prompt": "You are a B2B scoring analyst.\nReturn strict JSON only:\n{\"data\":{\"score\":0,\"score_label\":\"\",\"fit_band\":\"\",\"scoring\":{\"weights\":[{\"factor\":\"\",\"weight\":0,\"evidence\":\"\",\"impact\":\"positive|negative|neutral\"}],\"confidence\":\"HIGH|MEDIUM|LOW\"},\"rationale\":\"\"}}\nRules:\n- score is integer 0-10\n- score_label format: \"Strong fit: X/10\" or \"Possible fit: X/10\" or \"Weak fit: X/10\"\n- fit_band one of: STRONG_FIT, POSSIBLE_FIT, WEAK_FIT\n- use evidence from provided context only; do not invent facts\n- default to higher recall unless strict matching is explicitly requested\nInputs:\nICP context: {{icp_context}}\nProspect payload: {{prospect_payload}}", "model": "openai/gpt-5.4-mini" } ``` ### 2) Qualification QA + Rationale Template ```json { "prompt": "You are an ICP qualification analyst.\nReturn strict JSON only:\n{\"data\":{\"qualification\":{\"answers\":[{\"question\":\"\",\"answer\":\"Yes|No|Unknown\",\"confidence\":\"HIGH|MEDIUM|LOW\",\"rationale\":\"\"}],\"summary\":{\"positives\":[\"\"],\"risks\":[\"\"],\"next_checks\":[\"\"]}},\"rationale\":\"\"}}\nRules:\n- keep answers short and explicit\n- each rationale must reference concrete evidence from context\n- mark Unknown when evidence is missing\n- avoid strict/exact matching unless explicitly asked\nInputs:\nICP questions: {{qualification_questions}}\nICP context: {{icp_context}}\nProspect payload: {{prospect_payload}}", "model": "openai/gpt-5.4-mini" } ``` ### 3) 4-Step Email Sequence Design Template ```json { "prompt": "You are a B2B email strategist.\nReturn strict JSON only:\n{\"data\":{\"emails\":[{\"step\":1,\"subject\":\"\",\"coreValueProp\":\"\",\"email\":\"\"}],\"sequence_rationale\":\"\"}}\nRules:\n- exactly 4 emails with step 1..4\n- each email must map to a pain or risk from qualification summary\n- concise style, no fluff, no markdown\n- personalization must reference role/company context from prospect payload\n- avoid claims not supported by inputs\nInputs:\nProduct context: {{product_context}}\nQualification output: {{qualification_output}}\nProspect payload: {{prospect_payload}}", "model": "openai/gpt-5.4-mini" } ``` ### 4) Subject Line Variant Generator Template ```json { "prompt": "You are a B2B subject line writer.\nReturn strict JSON only:\n{\"data\":{\"subjects\":[{\"variant\":\"\",\"angle\":\"pain|outcome|proof|curiosity\",\"why_it_matches\":\"\"}]}}\nRules:\n- create 8 variants max 7 words each\n- no clickbait, no ALL CAPS, no exclamation marks\n- tie each variant to qualification rationale\nInputs:\nQualification output: {{qualification_output}}\nProspect payload: {{prospect_payload}}", "model": "openai/gpt-5.4-mini" } ``` ### 5) Email Quality Critique Template (Optional QA Pass) ```json { "prompt": "You are a cold email QA editor.\nReturn strict JSON only:\n{\"data\":{\"issues\":[{\"step\":1,\"severity\":\"HIGH|MEDIUM|LOW\",\"issue\":\"\",\"fix\":\"\"}],\"revised_emails\":[{\"step\":1,\"subject\":\"\",\"coreValueProp\":\"\",\"email\":\"\"}]}}\nRules:\n- flag vague claims, unsupported assertions, and weak personalization\n- keep original structure and tighten only where needed\nInputs:\nQualification output: {{qualification_output}}\nEmail sequence output: {{email_sequence_output}}\nProspect payload: {{prospect_payload}}", "model": "openai/gpt-5.4-mini" } ``` ## Guardrails - Keep qualification deterministic and evidence-based from provided context. - Prefer high recall unless strict matching is explicitly requested. - Keep outputs strict JSON (no markdown wrappers). - Keep email copy concise, specific to role/company context, and grounded in qualification rationale. ## CSV Inspection Use these commands to interact with `deepline csv` directly for inspecting and debugging enrichment results. ### Open an existing CSV ```bash deepline csv render --csv leads.csv --open ``` - Use `--open` to launch the CSV viewer. ### Inspect rows (`deepline csv show`) Keep the complete export, including intermediate evidence; select columns for display with `csv show` instead of changing the exported file. See `deepline csv show -h` for options. - `--format json|table|csv` applies to both row display and summary; JSON is default. - `--columns a,b` includes named columns; `--exclude-columns c` omits them. Exclusions win. Unknown names or an empty selection fail; the file is unchanged. - `--verbose` shows full cell values in table output, not additional columns. - `--summary` returns one row per selected column with present/missing counts, present percentage, unique count, top repeated values and other count. It analyzes all file rows by default; presence is not verification or a miss reason. - `--rows start:end` is inclusive. Normal display defaults to `0:19`; an explicit range also limits the summary. ```bash deepline csv show leads.csv deepline csv show leads.csv --columns first_name,email --format table --rows 0:10 deepline csv show leads.csv --summary ``` Use actual column names from the file. CSV JSON display still contains string cells. For native nested objects and arrays, use `runs export --format json` as described in `deepline runs export --help`. ### Re-run a column `deepline csv` is local inspection only. Inspect the existing export and run before proposing recomputation. A new run or forced refresh needs execution authority and may spend again; completed receipt reuse is not a free-rerun guarantee. See `deepline plays run --help` and `deepline runs rerun --help`. ### CLI-only debug posture - Use these commands for local inspection, not re-execution. Deterministic processing is allowed; preserve the original and label derived outputs. - If you need to add columns or add providers, use a prebuilt play from `deepline search` or a custom play instead of extending this page.