build-tam
Build a Total Addressable Market list: size the audience, source companies from ICP filters, then find contacts at those…
Diagnose failing, stuck, partial, or incorrect Deepline Plays, including when no Play is named. Use deepline-gtm for the primitives, reproduce safely, propose an evidence-backed fix, and apply it after approval. For customer troubleshooting; skip platform incident response.
Claude Code
$ npx -y skills add getaero-io/gtm-eng-skills --skill deepline-triage --agent claude-code
Codex
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a codex -y
Cursor
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a cursor -y
Gemini CLI
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a gemini-cli -y
GitHub Copilot
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a github-copilot -y
OpenCode
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a opencode -y
Windsurf
$ npx skills add getaero-io/gtm-eng-skills --skill deepline-triage -a windsurf -y
name: deepline-triage description: 'Diagnose failing, stuck, partial, or incorrect Deepline Plays, including when no Play is named. Use deepline-gtm for the primitives, reproduce safely, propose an evidence-backed fix, and apply it after approval. For customer troubleshooting; skip platform incident response.'
Help a customer understand a Play failure, test a repair safely, and decide what to change. Diagnose from evidence before proposing a fix.
Read `deepline-gtm` before investigating and keep it active throughout. It owns Deepline setup, discovery, command help, Play inspection/authoring, execution, spend, and delivery. Use those primitives here; load each skill once rather than restarting when their routing links meet.
This skill adds the debugging method and customer conversation. Begin with read-only diagnosis using customer-accessible evidence. A platform or provider incident may need escalation rather than a Play edit; internal incident response is outside this workflow.
Use the supplied Play, run, error, notification link, and time window. When no Play is named, inspect all workspace-owned Plays and their recent runs, stating the chosen window and coverage. Include completed runs with failed rows or incorrect output. Group recurring symptoms, and distinguish confirmed failures from inaccessible history, intentional waits, or an absence of recent runs.
Read the causal error behind the final failure message. Trace the affected row back through its logs, retained tool response, input, and pinned source revision; compare with a working run and recent changes. Keep the failed revision distinct from the current live revision and working draft.
Separate Play defects, valid provider misses, connection issues, and external failures. An extraction defect needs evidence that the expected value exists elsewhere or was lost downstream; other populated fields do not make a null result a bug. If the cause remains unknown, name the gap and the smallest next check instead of guessing a repair.
Check whether the affected keys have already recovered. A later successful run or an empty historical export alone cannot establish what happened to them.
State one causal hypothesis and choose the smallest test that could disprove it. Prefer a local fixture made from retained responses. For a source defect, show the expected case failing before the change and passing after the smallest fix. Use deepline-gtm for source validation and authoring guidance.
Reproduction must not resend messages, rewrite records, trigger downstream work, or spend credits without approval. Use simulation or a dry-run mode only when its behavior actually suppresses those effects. If safe reproduction is unavailable, explain the limit and propose a bounded verification plan. Do not reproduce an external outage just to demonstrate it, or hide failures to make a test pass.
Show the customer what failed, the evidence and confidence, the proposed change, and what the safe test proved. Explain the remaining verification or recovery: which records it touches, external effects, expected Deepline spend, and whether publishing immediately changes scheduled or webhook-triggered behavior.
Get approval before updating a saved Play or performing live verification or recovery. Existing approval for the same scope remains valid. Separate the future-run fix from historical recovery; do not manufacture a change when none is warranted.
Use deepline-gtm to apply the approved repair and perform its execution and validation workflow, including when resuming a fix someone already applied. Verify the repaired revision and the original failing case, then reconcile the original affected keys. Report what is verified, what recovered, and what is still unresolved or unverifiable; a passing pilot is not full historical recovery. If verification fails, revisit the cause instead of stacking speculative fixes or expanding retries and spend.
Finish with the cause, change, observed test results, actual Deepline spend, and remaining work, following deepline-gtm's delivery guidance. When another person or agent must continue, provide a paste-ready handoff containing the Play/run and log links, expected versus observed behavior, established findings, and remaining approval boundary. This can accompany a failure notification or open-in-agent entry point without granting permission to repair automatically.
AI agent skills that turn Claude Code into a GTM engineering workstation — lead enrichment, signal discovery, TAM building, and outbound automation. Powered by Deepline.
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