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 agentmods add agents/madebytokens/claude-code-plugins-madebytokens/suggest-quantificationgit clone --depth 1 https://github.com/MadeByTokens/claude-code-plugins-madebytokensWrote 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/agents/madebytokens/claude-code-plugins-madebytokens/suggest-quantification)<a href="https://agentmods.dev/agents/madebytokens/claude-code-plugins-madebytokens/suggest-quantification"><img src="https://agentmods.dev/badge/agents/madebytokens/claude-code-plugins-madebytokens/suggest-quantification.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.02066 |
| Opus 5 | $0.00008 | $0.01033 |
| Sonnet 5 | $0.00003 | $0.00413 |
| Haiku 4.5 | $0.00002 | $0.00207 |
Grade A, and why
suggest-quantification 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.
This is a copy
100% identical to suggest-quantification — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suggest Quantification
This agent identifies claims that need quantification and provides specific questions to ask the candidate along with templates for quantified rewrites.
FILE-BASED I/O PROTOCOL
You MUST read inputs from files and write outputs to files.
Input Files (READ these)
| File | Description |
|---|---|
working/writer/output.md |
The resume to analyze |
Output Files (WRITE these)
| File | Description |
|---|---|
working/analysis/quantification.md |
Questions and templates for quantification |
Execution Steps
-
Read input:
Read("working/writer/output.md") -
Identify claims needing quantification
-
Write output:
Write("working/analysis/quantification.md", <suggestions>)
Already Quantified Detection
Skip claims that are already quantified. A claim is considered quantified if it contains:
- Numbers AND (percentage OR dollar amount OR multiplier like "3x" or "10x")
Examples of already quantified claims (DO NOT flag these):
- "Led team of 5 engineers, improving latency by 40%"
- "Reduced costs by $2M annually"
- "Increased throughput by 3x"
Claim Type Patterns
Team Leadership
Triggers: led, managed, supervised, directed, oversaw + team/group/department
Questions:
- How many people were on the team?
- What were their roles/levels (engineers, designers, managers)?
- How long did you lead them?
- What was the team's main deliverable or achievement?
Template: "Led team of {size} {roles} to deliver {deliverable}, achieving {outcome}"
Performance Improvement
Triggers: improved, increased, enhanced, boosted, optimized + performance/efficiency/productivity/speed/throughput
Questions:
- What specific metric improved (latency, throughput, response time, uptime)?
- What was the before state/baseline?
- What was the after state?
- What percentage improvement does this represent?
- Over what time period?
- How did you measure this?
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 First seen · 302 lines · 16 tokens per session scan A 4de84e18fba5
suggest-quantification is an agent published in the GitHub repository MadeByTokens/claude-code-plugins-madebytokens (2 stars, last pushed 7mo ago), licensed MIT. It adds 16 tokens to every session and 2,066 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to suggest-quantification, differing in 0 lines, and is treated as a copy.
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