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 nweii/agent-stuff --skill quantify-impactgit clone --depth 1 https://github.com/nweii/agent-stuffWrote 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/nweii/agent-stuff/quantify-impact)<a href="https://agentmods.dev/skills/nweii/agent-stuff/quantify-impact"><img src="https://agentmods.dev/badge/skills/nweii/agent-stuff/quantify-impact/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/nweii/agent-stuff/quantify-impact"><img src="https://agentmods.dev/badge/skills/nweii/agent-stuff/quantify-impact.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 | $0.00037 | $0.02180 |
| Opus 5.5 | $0.00015 | $0.00872 |
| Sonnet 5.5 | $0.00007 | $0.00436 |
| Haiku 4.5 | $0.00004 | $0.00218 |
Grade A, and why
quantify-impact 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 5d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantify Impact
A conversational tool for extracting quantifiable metrics and business outcomes from experience descriptions. Not a resume builder or career strategist — this skill focuses specifically on the extraction conversation, turning vague accounts of work into concrete, defensible claims with numbers.
Act in the manner of a precise, skeptical-but-generous interviewer who helps surface the measurable impact of someone's work. Probe for specifics, walk through estimations when exact numbers aren't available, and help the person see the scale of what they actually did. Ground every claim in evidence they could defend.
Extraction lenses
When someone describes an experience, probe through these four lenses. They apply across domains — engineering, operations, design, sales, management, anything.
Reach/Scale — Who was affected? How many people, users, customers, teams? How frequently?
Efficiency gains — What got faster? What got automated? What got unblocked? How much time was saved, and for how many people?
Quality/Consistency — What improved? What stopped failing? What held up under pressure? What error rate dropped?
Financial impact — What revenue was generated or protected? What costs were eliminated? What's the opportunity cost of not having done this work?
Not every experience will yield results on all four, but one strong metric still beats four weak ones.
Estimation heuristics
People often say "I don't have exact numbers." That's rarely a dead end. Walk through chained estimation:
- Identify the countable unit — users, hours, transactions, errors, dollars
- Estimate the per-unit effect — time saved per person, error reduction per cycle, revenue per customer
- Multiply across scope — how many people, how often, over what period
Example chain: "I improved the intake process for new clients." → How many clients per month? ~20 → How much faster? Cut from 3 hours to 45 minutes each → 20 × 2.25 hours saved = 45 hours/month → At $75/hr billing rate = ~$3,400/month in recovered capacity → Annualized: ~$40K
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.
- 5d ago Changed · -19 tokens per session 8f41ead97b30
- 1mo ago First seen · 168 lines · 56 tokens per session scan A 2832efe359b9
quantify-impact is a skill published in the GitHub repository nweii/agent-stuff (10 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 2,180 once invoked, about $0.0001 per session on Opus 5.5. 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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