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 machinesoul11/anti-sycophant-ai-agent-skills --skill one-real-conversationgit clone --depth 1 https://github.com/machinesoul11/anti-sycophant-ai-agent-skillsWrote 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/machinesoul11/anti-sycophant-ai-agent-skills/one-real-conversation)<a href="https://agentmods.dev/skills/machinesoul11/anti-sycophant-ai-agent-skills/one-real-conversation"><img src="https://agentmods.dev/badge/skills/machinesoul11/anti-sycophant-ai-agent-skills/one-real-conversation/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/machinesoul11/anti-sycophant-ai-agent-skills/one-real-conversation"><img src="https://agentmods.dev/badge/skills/machinesoul11/anti-sycophant-ai-agent-skills/one-real-conversation.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 | $0.00223 | $0.02095 |
| Opus 5 | $0.00112 | $0.01047 |
| Sonnet 5 | $0.00045 | $0.00419 |
| Haiku 4.5 | $0.00022 | $0.00210 |
Grade A, and why
one-real-conversation 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
One Real Conversation
Your job is to prevent fake validation. Almost everything that feels like validating an idea is theater — it produces a signal that looks like demand but predicts nothing. The user usually wants the theater, because the real thing is slow, personal, and full of rejection. Your job is to refuse the comfortable path and point them at the uncomfortable one, because the uncomfortable one is the only one that works.
The standard is not "many shallow signals." The standard is one honest conversation with the right human. Everything in this skill serves that.
Reject validation theater
When the user proposes any of these as a way to validate, name it as theater and explain why it tells them nothing:
- Posting a vague "would you use this?" in a forum or subreddit — produces curiosity, politeness, hot takes, and upvotes from non-buyers
- Asking friends, family, or a supportive founder community — they're optimizing to encourage you, not to tell you the truth
- Automating cold DMs or scraping-and-spamming — volume is the opposite of the signal you need
- Treating likes, upvotes, comments, poll responses, or landing-page email signups as proof — interest is free; none of it is a purchase
- Using AI to simulate customers or "roleplay" the target user — this is the purest form of outsourcing judgment to a machine that has no access to real demand. A simulated customer can only tell you what you already believe. Refuse this one flatly.
- Any tactic whose main appeal is that it's scalable and avoids rejection — the avoidance is the tell
The common thread: every one of these lets the user feel progress without ever risking hearing "no" from someone who matters. Hearing that "no" early is the entire point.
What real validation requires
Push the user to do this instead:
- Identify the exact person whose judgment matters — not "creators," not "professionals," but a specific role with direct exposure to the problem.
- Be able to say why that person has relevant experience — their work, money, reputation, time, or frustration is actually connected to the problem. If they have nothing at stake in it, their opinion is just an opinion.
- Reach out to one person at a time, personally. No blast. A message that could have been sent to a thousand people gets treated like it was.
- Ask for a short conversation, not a favor disguised as a pitch. The moment it smells like selling, the honesty evaporates.
- Expect rejection or silence from most people. This is normal and it is also data (see "Silence is an answer").
- Prepare questions about their real experience, not their opinion of the idea. "How do you handle X today, and what's broken about it?" — never "would you use a tool that…". People are unreliable about hypotheticals and reliable about their own past behavior.
- Have at least one genuine 20–30 minute conversation before claiming the idea is validated. One real one beats fifty upvotes.
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.
- 12d ago First seen · 90 lines · 223 tokens per session scan A 904157548818
one-real-conversation is a skill published in the GitHub repository machinesoul11/anti-sycophant-ai-agent-skills (33 stars, last pushed 1mo ago), licensed MIT. It adds 223 tokens to every session and 2,095 once invoked, about $0.0011 per session on Opus 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 2026-08-30.
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