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 closedloop-ai/claude-plugins --skill measurement-disciplinegit clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote 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/closedloop-ai/claude-plugins/measurement-discipline)<a href="https://agentmods.dev/skills/closedloop-ai/claude-plugins/measurement-discipline"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/measurement-discipline/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/closedloop-ai/claude-plugins/measurement-discipline"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/measurement-discipline.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 | $0.00071 | $0.02279 |
| Opus 5.5 | $0.00028 | $0.00912 |
| Sonnet 5.5 | $0.00014 | $0.00456 |
| Haiku 4.5 | $0.00007 | $0.00228 |
Grade A, and why
measurement-discipline 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 4d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measure One Thing, Then Write It Down
When invoked
Apply the rules below to the measurement or optimization task at hand: baseline before touching anything, change one variable, keep a control where the harness could drift, require a mechanism for any claimed win, and record a loss as REFUTED rather than letting it go unwritten. If the project keeps a measurement log, search it by symptom before proposing or diagnosing anything, then append the resulting entry, success or refutation, as part of finishing the task.
Use this as team practice, or paste it into an AI agent's instructions; it is written as instructions either way. It applies to anything measurable: query latency, cache hit rates, build times, bundle size, model parameters, cost per request. Its purpose is to make every optimization question cost one search instead of one rediscovery, and to make sure the answer you find was honestly obtained.
The rules
1. Baseline before you touch anything, and get the noise floor from repeated runs. One before-number and one after-number cannot tell a real effect from a slow afternoon. Discard the first run as warmup, take at least five or six more, record every one, and state the median and the spread. The spread is your noise floor; nothing smaller is a result.
2. Change one variable. When two things change together and the number moves, you have learned nothing about either, and you will be back here next quarter to learn nothing again. If you must bundle changes to ship, still measure them one at a time first.
3. Keep a control when the harness could drift. Machines get busy, caches warm, background jobs start; a control on a path your change cannot possibly touch tells you how much of the delta was the world rather than you. If the control moves as much as the candidate, your noise floor is wider than you thought, and you say so.
4. A win must beat the noise floor AND come with a mechanism. A delta without a mechanism is a coincidence you will re-litigate, because nobody, including you in six months, can tell it apart from luck. Name what changed: call count went from 1 + N to 2, the plan switched to an index scan, the allocation left the hot path. No mechanism means the experiment is not finished.
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.
- 4d ago First seen · 151 lines · 71 tokens per session scan A 1859b7dcfb87
measurement-discipline is a skill published in the GitHub repository closedloop-ai/claude-plugins (122 stars, last pushed today), licensed Apache-2.0. It adds 71 tokens to every session and 2,279 once invoked, about $0.0003 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 2026-10-06.
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