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×Use Claude Code with Tareno to turn implementation notes and customer-safe evidence into a LinkedIn case study, with every claim sourced and every post approved in Tareno.
Yes. Claude Code reads your design notes, the relevant pull requests and the measurements you are allowed to publish, then drafts a case study with the problem, the decision and one lesson, and saves it through Tareno. It flags every claim that your notes do not support. Scheduling creates a pending request that you approve in Tareno.
Last reviewed 2026-10-24 · Markdown version for AI agents
Engineers learn from other engineers' case studies, and LinkedIn is where hiring managers and peers read them. The hard part is not the writing; it is deciding what you may disclose and proving what you claim. Claude Code is close to the evidence: it can read the pull request that made the change, the benchmark script and the notes written at the time. This page covers a payments-infrastructure example, with the checks that keep a technical case study honest and the approval step that keeps legal and the author in the loop before anything is published.
Imagine a payments team that cut its nightly reconciliation job from four hours to forty minutes. The tech lead opens Claude Code in the repository and points it at the design document, the pull request and the benchmark output, and adds a rule: no customer names, no volumes, no hostnames. Claude Code drafts a 250-word case study with the problem, the two options that were considered, the decision and one lesson about batching. It marks the sentence with the "four hours to forty minutes" figure as supported by the benchmark file and flags a claim about "99.9 percent fewer errors" as unsupported. The lead removes it, Claude Code saves the draft through create_drafts for the company page, and the legal reviewer reads it in Tareno before the lead requests a Wednesday slot.
Before any draft, write the rules: no customer names, no volumes, no internal hostnames, no unreleased products. Claude Code applies them as a filter, and you can ask it to list what it removed.
Name the design document, the pull request and the benchmark output. A case study should rest on files that exist, so the claims can be traced to a line.
Ask for that structure in about 250 words. The options you rejected are what make a case study useful to other engineers, so insist on them.
Have Claude Code list every figure and causal claim in the draft with the file and line that supports it, and mark the rest as unverified. Remove or soften anything that has no source.
Claude Code confirms the LinkedIn account with list_accounts and saves the draft through create_drafts. Let the reviewer read it in Tareno, where the exact text is visible.
After review, ask for a date, a time and an IANA time zone. The request waits as a pending action until the approver confirms it in the dashboard.
Read docs/design/reconciliation.md, pull request [number] and the output of scripts/bench.sh. List the figures that are supported by these files and the claims in them that are not, and apply this disclosure rule: no customer names, volumes or hostnames.Write a 250-word LinkedIn case study with four parts: the problem, the two options we considered, the decision and one lesson. Use only the supported figures, write for [engineering managers] and keep marketing language out of it.List every number and every causal claim in the draft, each with the file and line that supports it. Mark everything without a source as unverified, propose a softer wording for each and do not save anything to Tareno yet.Use Tareno to confirm the LinkedIn company page [name] and save the approved text as a draft for it. Return the draft ID and the text, and do not request a time until I say the legal review is done.As configured in Tareno on 2026-10-24. The live rules for your own account come from the get_platform_schema tool.
Source: Microsoft Learn: LinkedIn Posts API, read on 2026-10-24. Platform rules change; check the page before you rely on a number.
https://tareno.co/api/mcpUse the same Tareno account across supported clients. Open the relevant setup guide for authentication, first use, reconnect, and revocation.
list_accountsChecked:
Yes. Point it at the pull request, the design notes and the measurements you may publish, and it drafts the problem, the options, the decision and a lesson. It marks claims your files do not support, which you then remove or soften.
Write the disclosure rules into the prompt and ask Claude Code to list what it removed. Then read the draft in Tareno. Tareno only receives what Claude Code sends, so the check is a read of the draft and every image.
It can prepare drafts and requests for any LinkedIn account connected in Tareno. LinkedIn's documentation says a company page needs one of the page roles, such as administrator or content admin, for the connecting person.
Technical readers check numbers. A table of claims with file and line lets a reviewer verify the post in minutes, and it exposes statements that sounded plausible in the draft but have no source behind them.
Yes, as an image that is hosted at a public HTTPS address or already in your media library. Tareno's configuration lists up to 20 images per LinkedIn post, but each image should be reviewed for internal names first.
No. Scheduling requests become pending actions in Tareno, so the approver reads the exact text in the dashboard before LinkedIn receives anything. Claude Code can only report that the action is pending.