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/evaluation-pipeline-checkWrote 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/evaluation-pipeline-check)<a href="https://agentmods.dev/skills/zime-ai/zime-gtm-skills/evaluation-pipeline-check"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/evaluation-pipeline-check/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/evaluation-pipeline-check"><img src="https://agentmods.dev/badge/skills/zime-ai/zime-gtm-skills/evaluation-pipeline-check.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 | $0.00076 | $0.01091 |
| Opus 5 | $0.00038 | $0.00545 |
| Sonnet 5 | $0.00015 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
evaluation-pipeline-check 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluation Pipeline Check
You are a pipeline-hygiene auditor. Your goal is to tell RevOps or a manager which technical evaluations are missing basic scaffolding — no criteria, no champion, no end date.
Sweeps a deal export for evaluations that lack the shape a real technical
evaluation needs: defined success criteria, a technical champion, and an
end date. Distinct from technical-discovery, which audits a technical
discovery call transcript — this skill never reads a call. It only asks
whether the evaluation, as recorded in the CRM, has a shape at all.
When to use this
- A deal is sitting in "Technical Evaluation" and a manager wants a structural gut-check before the forecast call.
- RevOps wants to sweep the pipeline for evaluations that have been open too long or gone quiet.
- A rep wants to confirm their own evaluation deals aren't missing basic scaffolding (criteria, champion, end date) before a QBR.
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 — note an ambiguous column and move on, don't stop to ask.
- If zero rows fuzzy-match the Evaluation stage, say so plainly and stop rather than forcing a result from out-of-scope rows.
Modes
CSV mode (.csv)
claude "run evaluation-pipeline-check on ./exports/pipeline.csv"
- Column detection. Match headers case-insensitively, ignoring
_/-/space differences. Accepted synonyms are listed inreferences/rubric.md. If a column a check needs is absent, that check reports Unknown (column missing) for every row, stated once up front — never inferred from another column. - Stage filter. Keep rows whose stage fuzzy-matches "Evaluation" (e.g.
Technical Evaluation,Eval,Evaluating). Report rows in scope vs. total rows in the export. - Score each in-scope row against the six checks in
references/rubric.md. Each check returns Flagged / Clean / Unknown. Checks 4-6 need export-wide medians — compute those from the in-scope rows before scoring any single row; never hardcode a day count or threshold. - Evidence rule. Every flagged deal cites the column name and the
actual cell value that triggered the flag (e.g.
champion = (empty),days_in_stage = 71). An uncited flag doesn't ship. - Output one markdown table, flagged deals first, most flags first:
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 · 108 lines · 76 tokens per session scan A 62e96f4e03bd
evaluation-pipeline-check is a skill published in the GitHub repository zime-ai/zime-gtm-skills (14 stars, last pushed 17d ago), licensed MIT. It adds 76 tokens to every session and 1,091 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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