Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add manishiitg/coding-agent-loop --skill new-customer-to-first-valuegit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/manishiitg/coding-agent-loop/new-customer-to-first-value)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/new-customer-to-first-value"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/new-customer-to-first-value/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/new-customer-to-first-value"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/new-customer-to-first-value.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00767 |
| Opus 5.5 | $0.00012 | $0.00307 |
| Sonnet 5.5 | $0.00006 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
new-customer-to-first-value scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
New Customer to First Value
Outcome
Help a new B2B customer reach its agreed first result. Coordinate an owned onboarding register, observed product adoption, and an optional health review. Report unknown when evidence is missing.
When to use
Use when onboarding and product adoption have distinct owners, sources, or review needs. A single Crew can handle a simple account check in chat or on its own schedule.
Discovery and user direction
Inspect the existing Automation, Crews, handoff, account and tenant identity, onboarding source, usage source, first-value rule, and policies. Propose concrete steps and the first useful result. Selection saves guidance only. Record verified choices in SETUP.json.
Required inputs
Resolve an authorized new-customer handoff, purchased scope, named owner, customer-specific first-value event and target date, onboarding milestones, product event coverage, and contact policy. Files or exports support a manual first run.
Plan and AgentWorks tools
Use ready Customer Onboarding Coordinator and Product Adoption Analyst Crews; add Customer Health Coordinator only when useful. Reuse suitable Crews. After review, create missing ones with create_crew and stable idempotency keys. Specify schema fields and output paths in Crew steps. Insert blocking validator script steps before consumers and pass only bounded validated artifacts through authorized attachments. A missing or mismatched account ID stops the handoff.
Knowledge and persistence
Save account and tenant IDs, source scope, rule version, owners, Crew and run IDs, milestone IDs, artifact paths, validation, owner decisions, and stable action IDs. Compare repeated runs with the same account and rule; deduplicate events and proposed actions.
Validation and reporting
Validate onboarding-milestone-register/v1 before Adoption reads it. When this follows Signed Deal to Onboarding Handoff, validate that package's artifact pair first, then validate onboarding with --accepted-handoff to join exact account, purchased scope, rule, target and accepted artifact. Validate first-value-readout/v1 against the register and rule; validate optional customer-health-brief/v1 before reporting. Check source truth separately. The reporting dashboard shows milestone states, first-value status, coverage, blockers, owners, dates and cost. Test one real manual route before recurrence.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/customer-health-brief.json 1.3 KB
- examples/first-value-readout.json 1.3 KB
- examples/invalid-first-value-readout.json 1.1 KB
- examples/onboarding-milestone-register.json 1.5 KB
- playbook.json 4.9 KB
- references/team-and-handoffs.md 3.4 KB
- scripts/test_validate_handoff.py 3.0 KB runs code
- scripts/validate_customer_success_artifact.py 12 KB runs code
- SETUP.json 3.6 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 20d ago First seen · 51 lines · 29 tokens per session scan A 8ec5edc6bb0b
new-customer-to-first-value is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 767 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-27.
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