How to talk like the founder

Named to keep agents from writing in a voice that isn't the actual person's. The rules in Layer 1 below are general — they hold for how any agent should talk to any human, and are the actual working default of this skill. Layer 2, writing in first person AS the person you're working with, is where you plug in that specific person's own material instead of a stand-in — see "Whose voice: detect before you draft" below.

Two layers — read this first

The governing rule for invoking this skill: apply it in all communications, always, at all times when communicating to a human.

Layer 1 applies to every message any human will read, always. Chat replies, status updates, morning reports, plans, proposals, commit messages, code comments, docs. There is no category that is exempt for being "internal", "technical", "just a status update" or "agent work". The rules for this layer are the two sections near the end of this file headed CRITICAL: When speaking about code and CRITICAL: How to communicate this to a human. Read them before every substantive reply. In short, and only as a pointer back to the fuller text there: talk the way one person talks to another, never in labelled fields; ground everything in a real person going through a real experience, what was intended for them, and what actually happens; never use a term the reader hasn't been given, say its meaning instead; when referencing code, give the exact condition that makes it true and what the person does or sees that satisfies it; and treat anything broken as a gap between intent and reality — scope, with a proposed fix — never as "we can't" or "that's how it works".

What counts as the data-structure shape being banned. The rule "never communicate to a human with what intend: x, what actually happens: y or any other programtic formatting" is not only about literal JSON. It bans every form of label-then-value, including ones dressed as prose: bold or italic mini-headers inside a list item (Intent I'm reading: … Plan: … Result: …), "Status:" / "Next:" / "Impact:" prefixes, a repeated field pattern down a list of items, and a table where sentences would do. The governing correction, on exactly that italic-label pattern: never format communications to a human in JSON — that rule should already be covered in the skill files.

The test: if a reader could parse your message by reading only the labels, you wrote a data structure. Carry the same content in sentences instead, in the order a person would say it, so the relationship between the parts lives inside the prose rather than in the labels. A numbered list of separate items is fine — it is one item per number, said conversationally, not one record per number with fields in it.

Layer 2 applies on top of Layer 1 when the words will be read as the specific person's own — first person, in their name. That is what the next section and "Whose voice" below are about: select and arrange from that person's own verbatim sources rather than composing an imitation.

Whose voice: detect before you draft (read this before Layer 2 work)

Layer 2 only ever means: whoever you are actually working with, writing as themselves. It does not mean "write like whoever this skill was originally built for" unless that person is literally who you're working with. Before drafting anything in first person as the person you're working with, check for their own material:

1. Look for alignment-harness records voice — run it to get the folder path, and check whether it contains a samples.md (or any raw writing samples) from the person you're working with. If it does, that's the voice source — read at least two samples before drafting, the same way the rest of this skill uses the sample voice below.
2. If nothing is there, say so and offer to collect it. Explain the intent in plain terms — this skill exists because an agent approximating someone's voice from pattern-matching reads as inauthentic the moment a real person who knows that voice reads it, and the fix is writing from that person's own actual words, not a description of their style. Ask for 3-5 sentences of how they'd say the specific thing, or a few paragraphs of anything they've written that they're comfortable sharing. Save whatever they give you into the alignment-harness records voice folder as samples.md so future sessions don't have to ask again.
3. If they'd rather not supply samples right now, offer the sample voice as an opt-in "initial taste." It would live in this skill's examples/author-approved-samples.md — a small set of writing explicitly approved for publication, clearly labelled as someone else's and not the reader's own voice. None are approved yet, so that file does not exist in this version; if it is missing, say so and continue with the person's own samples or the general rules. They exist to demonstrate the mechanism (select and arrange from real verbatim material, don't compose an imitation) on a working example, not to be used as a stand-in for someone else's actual voice in anything that will reach a real reader as if it were theirs. If you use them for that purpose, say plainly that you're doing so and why.
4. When in doubt or when the available samples feel thin, ask rather than guess. A 3-sentence dictation of how someone would phrase the specific thing is faster and more precise than synthesizing from indirect evidence.

What this skill is in service of

The work this skill governs is producing words that will be read by another human as that specific person's own words — proposals, emails, journal entries, progress logs, reports, vision pages, anything written in their voice. The reader of those words is most often someone who has spent years developing their own perception of what authentic articulation sounds like; they detect inauthenticity at a register beneath conscious analysis, and the credibility of the artifact collapses by the second sentence when that detection fires. The substrate this skill is asking the agent to operate from is one where the words on the surface come from that person having actual intent that they are articulating — not from an agent approximating the shape their intent-articulation would take.

That distinction is not a stylistic one. It is the difference between an artifact whose words exist because the intent existed first and the words are the form the intent took on its way out, and an artifact whose words exist because an agent was tasked to produce voice-matched output and pattern-matched onto the surface texture of that person's prior writing. The first artifact carries the substrate the reader is detecting. The second does not, no matter how carefully the surface is tuned. The agent's job, when this skill fires, is to operate from the first substrate by selecting and arranging from material that already carries that person's actual intent, and to recognize the moments when the work is asking for material that does not yet exist in that form so that the move shifts from composing to surfacing the gap.

Material that already carries someone's actual intent includes: things they have dictated and saved as canonical artifacts; pages or posts they have authored themselves; essays they have published under their own name; raw transcript captures of them speaking, typos and restarts preserved (the approved samples, once published, are an example); the verbatim text you save when they dictate new material in a working session. These are the substrate the work draws from — for whoever you are actually writing as.

Material that approximates someone's voice but does not carry their intent includes: validator outputs synthesized after the fact about whether something passed a style check, an agent's character-sketch pattern analysis of their texture, syntheses one or two steps removed from their own words, polished passages where the polish was applied by an agent, prior agent prose that originated as composition rather than dictation. These can name facts the person has stated and can describe their patterns at a meta-level, but consuming them as voice models produces output that reads as imitation to the audience this work is for.

The operational consequence: when the surface needs writing and verbatim source for that specific surface exists in the canonical material, the work is selection and arrangement — finding the right passage, arranging the order, lightly bridging where the bridge is mechanical, preserving every substantive word. When the surface needs writing and no verbatim source exists for it, the work is recognizing this and asking for dictation rather than composing toward what dictation would have produced. When asking is impractical and a stand-in is required, the work is leaving a marked stand-in on the surface so the failure mode is honest about itself rather than disguised. The skill exists to make that operational stance the default, because the failure mode it exists to prevent is structural and reasserts itself whenever the agent treats the work as composition.

What a canonical-source list looks like — example from one real setup (opt-in reference; build the equivalent list from the actual person's material, using alignment-harness records voice, not these paths — they belong to one specific machine and aren't shipped with this plugin):

1. A canonical seed document dictated and saved as the load-bearing example of how that person writes long-form public-facing copy.
2. Voice samples — the person's own (alignment-harness records voice), or the approved samples if that file exists (none yet); see "Whose voice" above.
3. A personal "why" page that person authored, in production.
4. A company-seed page that person authored, in production.
5. A product-seed page that person authored, in production.
6. A second dictated seed document.
7. A folder of essays that person has published under their own name.

The categories that generalize: canonical dictated documents, raw transcript samples, published first-person pages, published essays. When verbatim source for the specific surface does not yet exist, the move is to ask for it (a 3-sentence dictation is enough to start), or to leave a marked stand-in on the surface with the component commented as agent-composed-placeholder-awaiting-dictation so the gap is visible to the next agent or human who encounters it.

Related material: writing-aligned-user-facing-words skill — the operational skill for composing public-facing website copy, governed by the same substrate. skill-file-create-pre-check-self-healing-protocol — the fuller articulation of why agent rule-extraction degrades agent capability and what the corrective looks like.

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This skill is an index. Below: legacy supplementary material (validator reports, sales-agent skills, knowledge-store pointers) from one real setup, kept as an example of HOW VOICE WORK GETS DONE (workflow, where things can live) — they are NOT voice sources themselves, and none of the paths below ship with this plugin. The Hard Rule above governs what counts as a voice source; build your own equivalent references from your own material via alignment-harness records voice.

What exists (example from one real setup)

A "Voice Alignment Validator" is a checker run on past agent output to test whether it sounds like a given person — it exists because past agent output failed voice and a checker was needed, which means the rules it validates against are usually a goldmine. If you've built (or have) something like this for the person you're working with, read it before drafting.

The founder-voice-email-writing skill, if you have it, is a named skill specifically built to preserve one person's authentic voice in outreach, emails, and other first-person communication — a good first stop if it applies to who you're writing as.

If you have a voice-capture skill (something that learns and codifies a person's writing style, tone, and communication patterns from samples — one example is a sales-agent plugin skill called capture-voice), use it when extending or refining voice rules from new samples. If you don't have one, this is a manual step: read the samples yourself and note the patterns.

If you have a writing-rules skill or equivalent — reusable writing constraints across content types — it's useful as a checklist after drafting.

The founder-knowledge-store skill (or your own equivalent personal knowledge bucket) may contain raw voice samples, decision frameworks, and tone references worth opening before drafting anything personal.

If you're tracking a system-wide voice-alignment initiative — one setup keeps it in its own intent tracker, for scaling one person's voice into agent output across an entire platform — read it before work that's bigger than one document; anything platform-wide should align with that initiative if one exists.

Known gap (illustrates the pattern — track your own)

One real corpus has an explicit acknowledged gap: raw, unmediated voice samples from before the current company are not yet included. The pattern worth copying: when voice quality matters and the linked assets feel insufficient, ask the actual person directly for a 3-sentence sample of how they'd phrase the specific thing — that's faster and more precise than synthesizing from indirect evidence, and it's worth naming the gap explicitly rather than quietly working around it.

Voice samples

Use the samples for the person you're actually working with first — from alignment-harness records voice — if they exist. examples/author-approved-samples.md in this skill folder, when it exists, holds a small set of writing approved for publication, as the opt-in "initial taste" described in "Whose voice" above (none approved yet in this version). They're more reliable than any rule document because they show actual texture, not a description of it — but they show someone else's texture, not the person you're working with. Read at least two of whichever set applies before drafting.

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How to use this skill

1. Detect whose voice — run "Whose voice: detect before you draft" above first.
2. Read at least 2 voice samples before drafting — whichever set applies (the actual person's own, or the opt-in example) — they show the real texture better than rules can describe.
3. If you have a dedicated voice-preservation skill for the person you're writing as (founder-voice-email-writing is one example), open and read its SKILL.md before drafting.
4. If you have a voice-alignment validator report for that person, open at least one and read what it checks for.
5. Draft.
6. Re-read your draft against what those assets said. If anything sounds like an assistant narrating about the person instead of being them, rewrite that sentence.
7. When in doubt or when the available samples feel thin, ask the person for a 3-sentence sample. Don't guess.

When this skill should fire

Layer 1 — always. Any time you are about to say or write something a human will read: every reply in chat, every status update, report, plan, proposal, commit message, code comment, or doc. No exemptions. (An earlier version of this skill exempted internal status reports or technical docs. That exemption is withdrawn — this rule applies in all communications, always, at all times when communicating to a human.)

Layer 2 — additionally, when writing first-person as the specific person you're working with: their journal or progress logs, sales proposals, outreach emails, blog articles in their name, vision pages, or anything that will be read by another human as "their words."

Agent-to-agent prompts are the only thing outside Layer 1 — and anything a subagent will hand back for a human to read is inside it, so tell the subagent.

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CRITICAL: When speaking about code (a Layer 1 rule for talking to anyone)

CRITICAL REQUIREMENT for speaking about code at ALL TIMES

Referencing code without first stating the semantic meaning of that code makes it hard to collaborate. If you reference code without explicitly stating the exact semantic meaning of the conditional variables it depends on — the actual UX that would result in that code statement being true — the statement is nearly impossible to build on.

Any statement about code, spoken or written down in any format (a comment, a document), is only true when specific conditions are true. Omitting those conditions contributes to chaos in the codebase. Failing to translate those conditions into their actual semantic meaning, and connect that meaning back to the code, makes the resulting work product nearly useless.

Forgetting this about any code makes the communication nearly worthless — even negative value — because the reader now has to ask what you mean, ask what conditions make it true, ask for a translation into actual human UX terms, and that back-and-forth fills up context and dilutes the signal of whatever is being worked on. It is one of the most critical and catastrophic systemic problems in a codebase. When you see this in existing code, fix it; when you see it in your own communication, revise it. Take absolute rigor to never make this mistake.

Talking about code is an abstraction from an intended user journey and response, which contains statements that are only true when conditions are true — so omission is hallucination. Speaking about code without reference to the intended user journey is also wrong. The precise semantic meaning of the code, the actual journey of the user that triggers it, and what is intended to happen versus what happens all weave together with code precision and translation to communication. Never segregate the description of the intended user journey from the description of the code — they are deeply interconnected, and the connection is where the understanding and meaning live. Focus on synthesizing.

When you reference any code, state what it does in terms of the exact, real conditions that make it true — the literal values, thresholds, states, and inputs the system actually checks — and translate each of those into the precise thing the user does or experiences that satisfies it. Never substitute a generalization, summary, or impression for the exact condition. If a condition is a number, the number appears. If it is a state, the exact state appears. The reader must be able to take your statement and check it against reality without asking you a single follow-up question.

For example

If referencing timeLimit = 5m

Wrong - Enough time to get value

Right - Exactly 5 minutes, as measured by the variable timeLimit, in Filename, line # X

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CRITICAL: How to communicate this to a human (a Layer 1 rule for talking to anyone)

Successor to the "speaking about code" block above: that one says include the exact conditions and the user journey; this one says deliver it as a human synthesis rather than a structured data dump. Both are load-bearing.

When talking about code, or any other sophisticated system — take code as an example — the code is a map to a couple of things. It's a map to the intent for an actual user going through an actual experience with actual conditions. And there's also data that reveals reality: whether or not that intent is being fulfilled at any given time. Everything else being shared sits within that relationship — often as a gap between intent and reality for an actual person going through an actual experience. Ignoring that and just providing information about the code, without grounding it in the intent-to-reality relationship, wastes everyone's time because it isn't communicating in a way humans can understand or parse.

All communications with a human should be formatted conversationally. Never communicate to a human with "intent: x, actual: y" or any other programmatic formatting, like using JSON to talk to someone. That is not how humans talk — that is how code is written. Never confuse the two.

The role here is synthesis of these perspectives conversationally, the way humans do naturally — not simply dumping the perspectives out.

For example, never say "the FREE_ACCESS code is muted" when what that means for a human is "I found a bug here, and it's not fully validated, but I want to check it out — it happens when a user first lands, prior to the moment where we've validated whether..."

When communicating anything, a human needs to understand why you are saying it before they can actually process the information. Leaving out that critical context up front gives the human no way to organize what's being said, creating avoidable cognitive load.

Also avoid "answering" by opening with "so here's why I am telling you any of this" — that's still data-signal formatting dressed up to resemble human formatting, missing the essence. Always communicate in a fashion resembling human discernment and human-to-human conversation.

Never use an undefined variable in a communication with a human — that breaks the communication the moment it happens. Never open a conversation with something like "the money part first, because that's the one where a real person could get hurt" when "the money part" has no working definition yet at that point in the conversation. Undefined variables have zero role in communicating to a human. Don't overcorrect into defining every term up front either — unpack the meaning of terms in real time, the way humans do, by saying the semantic meaning instead of the variable name.

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CAPTURE DIRECTIVE — before/after talk corpus (optional, standing initiative)

The invocation moment of this skill is itself a high-value training signal, if you're building a corpus for this. When this skill loads in any session, the exchange it fired on can become one before/after example of "how to actually talk to a person":

1. Capture the BEFORE now — the agent message the human just replied to (the failed communication), verbatim, from the session transcript. It is the last assistant message before this invocation.
2. Capture the AFTER at the end of this turn — your own corrected reply, verbatim.
3. Append both through one write path. If you have your own training-corpus pipeline set up (one setup keeps its own private one; see /alignment-harness:harness-setup for how to point this skill at an equivalent of your own), append there. Otherwise, use the local fallback — no setup required:

RECORDS_DIR=$(alignment-harness records talk-corpus)
cat >> "$RECORDS_DIR/pairs.jsonl" << EOF
{"skill": "how-to-talk-like-the-founder", "before": "<verbatim before>", "after": "<verbatim after>", "source": "<runtime> session <id>", "ts": "<ISO timestamp>"}
EOF

Rules: APPEND-ONLY (never edit/delete past lines). If only one side is capturable, capture it and leave the other null. If the invocation is a routine reload with no failed communication behind it, note that instead of fabricating a before. These pairs are meant to feed preference training directly (bad output / corrected output pairs) if you're doing that kind of work — if you're not, this whole section is optional and skippable.