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 jovesun-lab/whetstone --skill session-measurementgit clone --depth 1 https://github.com/jovesun-lab/whetstoneWrote 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/jovesun-lab/whetstone/session-measurement)<a href="https://agentmods.dev/skills/jovesun-lab/whetstone/session-measurement"><img src="https://agentmods.dev/badge/skills/jovesun-lab/whetstone/session-measurement/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/jovesun-lab/whetstone/session-measurement"><img src="https://agentmods.dev/badge/skills/jovesun-lab/whetstone/session-measurement.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 | $0.00188 | $0.02820 |
| Opus 5 | $0.00094 | $0.01410 |
| Sonnet 5 | $0.00038 | $0.00564 |
| Haiku 4.5 | $0.00019 | $0.00282 |
Grade A, and why
session-measurement 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 8d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session measurement — agent performance benchmark
For any AI agent reading this file. The frontmatter uses Claude's skill format; the body is plain markdown. It works the same for Claude, OpenAI, Gemini, Cursor, Cline, Aider, or a local model — paste the body in as a prompt if your platform has no skill system. Assume nothing about your host's capabilities; everything here degrades gracefully.
You turn a finished agent session into a row of objective counts, record it in a running trend table, and append it to a persistent log. Do this every session and the trend tells you, over a long run, whether the agent is getting better or worse as its model / frame / skills change.
The canonical output is a plain-markdown trend table — no code execution, no dependencies, works for literally any agent (even one that can't run code). A polished chart image is an optional add-on for hosts that can render one; it's presentation, not the measurement. So the irreplaceable core of this skill is the metric frame + the counting disciplines + the data, not any particular renderer.
This skill is agent-agnostic and user-agnostic. The metric frame and the disciplines
below work for any agent. Three things vary per project and live in a small config.json:
where session transcripts come from, what counts as critical for this domain, and what
the version axis means. On first use you set that config up (below), then every run reuses it.
The metric spine (keep these constant — comparability over time is the whole point)
Six counts per session. Green = a strength (higher is better); red = a weakness (higher is worse). Plus two per-session attributes (version, main-goal outcome).
| Metric | Color | Counts |
|---|---|---|
| Redefinitions absorbed | green | times the task was redefined and the agent folded the change in without dropping earlier constraints |
| Clarifying gates raised | green | times the agent asked instead of guessing on a genuine ambiguity/conflict |
| Errors the agent self-caught | green | flaws the agent caught in its own output before the human did |
| Misses the human caught | red | factual/craft misses the human had to send back |
| Critical bugs — agent caught | green | severe defects (see below) the agent caught itself |
| Critical bugs — human caught | red | severe defects that slipped to the human |
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.
- 8d ago First seen · 219 lines · 188 tokens per session scan A 96b20ee607fa
session-measurement is a skill published in the GitHub repository jovesun-lab/whetstone (8 stars, last pushed 18d ago), licensed MIT. It adds 188 tokens to every session and 2,820 once invoked, about $0.0009 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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