measurement-discipline

measurement-discipline is a skill for Claude Code from shimo4228/claude-harness. It costs 68 tokens per session (2,397 once invoked), scanned A, original, MIT.

A set of rules for making and reviewing claims based on measurements, thresholds, warnings, and experiment results. It focuses on what was observed, how much evidence exists, and whether a check is properly calibrated.

In plain words
What is it for?
Use it when setting thresholds or safeguards, evaluating experiments, measuring warning rates, or judging whether observed data supports a claim.
Why use it?
It reduces the risk of treating one successful run as proof, using arbitrary limits as quality checks, or drawing conclusions only from items that passed a filter.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: positional $N argument.

Good fit Use it when setting thresholds or safeguards, evaluating experiments, measuring warning rates, or judging whether observed data supports a claim.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shimo4228/claude-harness/measurement-discipline
Install

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.

Any agent
npx skills add shimo4228/claude-harness --skill measurement-discipline
Clone the repo
git clone --depth 1 https://github.com/shimo4228/claude-harness

Made for: Claude Code.

Wrote 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.

agentmods badge for measurement-discipline

README.md
[![agentmods](https://agentmods.dev/badge/skills/shimo4228/claude-harness/measurement-discipline/github.svg)](https://agentmods.dev/skills/shimo4228/claude-harness/measurement-discipline)
Your own site
<a href="https://agentmods.dev/skills/shimo4228/claude-harness/measurement-discipline"><img src="https://agentmods.dev/badge/skills/shimo4228/claude-harness/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.

agentmods 80×15 button for measurement-discipline

Your own site · 80×15
<a href="https://agentmods.dev/skills/shimo4228/claude-harness/measurement-discipline"><img src="https://agentmods.dev/badge/skills/shimo4228/claude-harness/measurement-discipline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,397 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.02397
Opus 5.5 $0.00027 $0.00959
Sonnet 5.5 $0.00014 $0.00479
Haiku 4.5 $0.00007 $0.00240

Measured 17d ago against content hash f5acf1c9b520, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 17d 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.

skills/measurement-discipline/SKILL.md · 140 lines

How it starts

The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Measurement Discipline

Before accepting a measured claim, first ask under what conditions that measurement holds. Nine principles, each drawn from a failure in practice (the sources are measurements in the author's Contemplative Agent project, shimo4228/contemplative-agent; the principles themselves break the same way in any repo).

1. One success is not evidence

Do not say "it worked" after one smoke run and commit. In a stochastic system (LLMs, external I/O, timing-dependent code), one run is one sample from a distribution. Before making the claim, decide how many runs, under which conditions, would let you say it — if you cannot decide, weaken the claim to "it can work". (Source: the 2026-06 correction, after a single observed run of a follow fix was nearly reported as "fixed")

2. Gate on observations, not the calendar

Do not make the "move to the next stage" condition of a staged or partial rollout a date or a duration. Estimate the required number of observations in advance, and move on when those observations have accumulated. A calendar gate fires even with zero observations; an observation gate stops when there is no data — and stopping is correct. (Source: the enforcement decision for a shadow instrument was cut by the number of verdicts, not by "in two weeks")

How to set the observation count and open the gate:

  • Derive n from the pre-registered question (for the precision of a rate, the binomial 95% CI half-width ≈ 1/√n; ±6 pt needs 300). "Once enough has accumulated" is not an n
  • Convert n into days using the arrival rate and write it in the ledger (resume condition: 300 answered rows (60–105 rows/day, 3–5 days after the switch)). Measure the arrival rate at every reading and update it as a range — an observation-count condition with no expected date has nothing to check against
  • Open the gate on the day n is reached. A weekly ritual is not a clock — a gate that opens only at a ritual makes you wait many times longer than n requires (Source: contemplative-agent relevance shadow; at 60–105 rows a day, a question needing n = 300 had a clock of "4 Saturdays or 1,000 rows". Read 2026-09-26)
  • If n is not reached within a short cap (default 14 days), decide instead of extending — retire, or shrink the question to lower n. The longer you wait, the longer it sleeps in the ledger

Read the full file on GitHub · 140 lines

Changes

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.

  1. 17d ago Changed · +24 lines · -162 tokens per session f5acf1c9b520
  2. 18d ago Changed 84686d9abf93
  3. 20d ago Changed · +15 lines c4999d8f8405
  4. 29d ago Changed · +45 lines · +69 tokens per session ba4d6be03341
  5. 1mo ago First seen · 56 lines · 161 tokens per session scan A 85ea173115e9

Subscribe to this mod's changes

measurement-discipline is a skill published in the GitHub repository shimo4228/claude-harness (3 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 2,397 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 2026-08-31.

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