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 mardab96/b2b-lead-generation-claude-skills --skill pipeline-hygiene-auditgit clone --depth 1 https://github.com/mardab96/b2b-lead-generation-claude-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/mardab96/b2b-lead-generation-claude-skills/pipeline-hygiene-audit)<a href="https://agentmods.dev/skills/mardab96/b2b-lead-generation-claude-skills/pipeline-hygiene-audit"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/pipeline-hygiene-audit/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/mardab96/b2b-lead-generation-claude-skills/pipeline-hygiene-audit"><img src="https://agentmods.dev/badge/skills/mardab96/b2b-lead-generation-claude-skills/pipeline-hygiene-audit.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 | $0.00056 | $0.01059 |
| Opus 5 | $0.00028 | $0.00530 |
| Sonnet 5 | $0.00011 | $0.00212 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
pipeline-hygiene-audit 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.
Pipeline Hygiene Audit
Use this skill when
The pipeline number is quoted in meetings and nobody quite believes it.
Pipeline decays quietly. A deal that was real in March sits in Proposal in August because nobody wants to be the person who closes it as lost. Multiply that by a year and the total at the top of the report stops meaning anything, which is usually discovered during a forecast conversation with someone senior.
Run this before the forecast, not after it goes wrong.
Required input
- A deal export with, at minimum: deal name or id, stage, value, created date, and last activity date.
- What the stages are supposed to mean.
Better with:
- expected close date, and whether it has been changed before
- owner
- source
- historical conversion rate between stages
- typical sales cycle length
Analysis workflow
- Run
../scripts/pipeline_hygiene.pyover the export. Counting stale deals, measuring stage ageing and comparing close dates against the sales cycle are arithmetic, and doing it by eye across a few hundred deals produces numbers that feel right and are not. - Age every deal against the typical sales cycle for its stage. A deal sitting in one stage for several times the normal duration has usually stopped moving rather than slowed down. Pick the multiplier from this store's own cycle rather than a rule of thumb.
- Find deals with no activity inside a reasonable window. No activity is the single strongest predictor of a deal that will never close, and it is usually visible months before anyone acts on it.
- Check the close dates. Dates in the past, dates that have been pushed more than twice, and dates that cluster suspiciously on the last day of a quarter each tell a different story about how the forecast is being constructed.
- Check stage definitions against stage behaviour. If deals routinely skip a stage or sit in one for a day, the stage is not doing any work and the pipeline reporting inherits that.
- Split the pipeline into what is defensible and what is decoration, and give both numbers. The gap between them is the actual finding.
- Produce a specific list to review, deal by deal, rather than a recommendation to "clean up the CRM", which never happens.
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 · 56 tokens per session scan A c599272883a2
pipeline-hygiene-audit is a skill published in the GitHub repository mardab96/b2b-lead-generation-claude-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,059 once invoked, about $0.0003 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 2026-08-31.
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