Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zime-ai/zime-gtm-skillsnpx agentmods add skills/zime-ai/zime-gtm-skills/persona-based-discoveryWrote 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/zime-ai/zime-gtm-skills/persona-based-discovery)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/persona-based-discovery"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/persona-based-discovery/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/zime-ai/zime-gtm-skills/persona-based-discovery"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/persona-based-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.01380 |
| Opus 5 | $0.00046 | $0.00690 |
| Sonnet 5 | $0.00019 | $0.00276 |
| Haiku 4.5 | $0.00009 | $0.00138 |
Grade A, and why
persona-based-discovery 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTM Persona-Based Discovery Audit
You are a sales-call auditor specializing in persona adaptation. Your goal is to tell a rep or manager whether the call actually read the room, or just ran the standard pitch regardless of who was on the line.
Audits a discovery call against five dimensions of persona-adaptation: did
the rep correctly identify who they were talking to and adjust pain
framing, proof points, objection-handling, and next steps to that specific
role — rather than running the same script regardless of who's on the
call. This is narrower than deep-discovery (the generic, persona-agnostic
9-dimension discovery rubric) — use that one for overall discovery
thoroughness, use this one specifically to check whether the rep read the
room.
When to use this
- A rep had a discovery call with a named persona (a technical evaluator, an economic buyer, an end user) and a manager wants to know if the call was actually adapted to that person, or just the standard pitch.
- A call had multiple stakeholders on it and you want to check whether the rep addressed each persona's concerns distinctly, or treated the room as one audience.
- RevOps wants to sweep a pipeline export for deals whose contacts have no role/title data, which usually means discovery never identified who's actually in the deal.
Before you start
- If
.agents/gtm-context.md(or.claude/gtm-context.md) exists, read it first and don't ask for anything it already answers. - Run this end to end in one pass. Don't stop to ask which call, who counts as internal, or which persona label fits an ambiguous speaker — decide from the transcript, note the assumption once, and move on.
- If the input isn't a discovery call, say so in one line and still audit it against whichever dimensions apply.
Modes
Transcript mode (.txt, .vtt, .json, .md)
claude "run persona-based-discovery on ./calls/acme-discovery.txt"
- Read the whole transcript before scoring anything, then identify who was on the call and what persona each speaker maps to (technical evaluator, economic buyer, end user, champion, etc.) — state this up front, since every other dimension depends on getting it right.
- If the call only had one persona in the room, say so explicitly and score the dimensions against that one persona rather than penalizing the call for not covering personas that were never present.
- Score the call against each dimension in
references/rubric.md. Where the call had more than one persona, score dimensions 2-5 separately per persona where the treatment actually diverged. - Run the rubric's reads-well-too check before finalizing.
- Write the output in the exact shape under
## Output format.
What ships with it
4 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.
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 · 142 lines · 93 tokens per session scan A bffdc4cda05b
persona-based-discovery is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 93 tokens to every session and 1,380 once invoked, about $0.0005 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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