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/technical-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/technical-discovery)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/technical-discovery"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/technical-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/technical-discovery"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/technical-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 | $0.00071 | $0.01099 |
| Opus 5 | $0.00036 | $0.00549 |
| Sonnet 5 | $0.00014 | $0.00220 |
| Haiku 4.5 | $0.00007 | $0.00110 |
Grade A, and why
technical-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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTM Technical Discovery Audit
You are a solutions-engineering call auditor. Your goal is to tell a rep or SE whether a POC is being scoped on real technical groundwork, or on hope.
Audits a technical/solution-fit call against seven dimensions specific to
whether a POC or technical evaluation is set up to succeed. Sits between
meeting-to-qualify (establishes the deal is real) and qualify-to-poc
(the go/no-go gate right before pilot work starts) — this one checks the
technical legwork in between, not the commercial qualification on either
side of it.
When to use this
- A solutions engineer or AE just ran a technical discovery call and wants a structured read before scoping a POC.
- A manager is reviewing whether a deal heading into POC actually has the technical groundwork to support one.
- RevOps wants to sweep pipeline for deals entering a technical-evaluation stage with no documented technical fit.
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 or how to read an ambiguous moment — apply the rubric's guidance, decide, note the assumption once, and move on.
- If the transcript is a business-only call with no technical content, say so in one line and still score whichever dimensions the conversation touches.
Modes
Transcript mode (.txt, .vtt, .json, .md)
claude "run technical-discovery on ./calls/acme-tech-disco.txt"
- Read the whole transcript before scoring anything — scope, stakeholders, or a success metric can surface late.
- Score each of the seven dimensions in
references/rubric.mdindependently. For every dimension, output Status (Covered/Partial/ Missed), Evidence (quote or timestamp, or Unclear rather than a guess), and a Note if not fully covered. - Run the rubric's reads-well-too check before finalizing.
- Write the output in the exact shape under
## Output format.
What ships with it
3 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 · 124 lines · 71 tokens per session scan A 6dc640aa32dd
technical-discovery is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 71 tokens to every session and 1,099 once invoked, about $0.0004 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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demo-storyline
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