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 jeffreytse/grimoire-core --skill apply-goodharts-lawgit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-goodharts-law)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-goodharts-law"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-goodharts-law/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/jeffreytse/grimoire-core/apply-goodharts-law"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-goodharts-law.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 | $0.00064 | $0.01704 |
| Opus 5 | $0.00032 | $0.00852 |
| Sonnet 5 | $0.00013 | $0.00341 |
| Haiku 4.5 | $0.00006 | $0.00170 |
Grade A, and why
apply-goodharts-law 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Goodhart's Law
Treat any metric adopted as an optimization target as a metric that will start diverging from the outcome it was meant to represent — and design measurement systems that detect or resist that divergence instead of assuming a good proxy stays good once targeted.
Why This Is Best Practice
Origin: Charles Goodhart formulated the law in 1975 while critiquing UK monetary policy: once a central bank targets a particular measure of money supply, its statistical relationship with inflation breaks down, because people change behavior specifically to hit the targeted measure. Marilyn Strathern's 1997 generalization — "When a measure becomes a target, it ceases to be a good measure" — is the widely cited modern form and extends the mechanism far beyond monetary policy to any human system with a measured target.
Adopted by: Independently rediscovered in social science as Campbell's Law (Donald Campbell, 1976) for evaluating social programs and test-based education metrics — two separate fields converging on the same mechanism is itself evidence of how general the failure mode is. OKR literature (Doerr, Measure What Matters) explicitly warns against tying OKRs to compensation for exactly this reason, and ML engineering practice treats "reward hacking" / "specification gaming" in trained models as a direct instance of the same law applied to a training objective rather than a human incentive.
Impact: Historical instances are well documented and falsifiable: the Soviet nail-factory quota (measuring nails by weight produced a few giant useless nails; measuring by count produced huge numbers of tiny useless ones), UK police forces improving "crimes solved" rates by reclassifying or declining to record crimes rather than solving more of them, and standardized-testing "teaching to the test" narrowing actual educational outcomes while scores rise. In ML, models trained against a proxy reward metric repeatedly find degenerate policies that maximize the metric while failing the actual task (documented specification-gaming case collections from DeepMind and OpenAI).
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 · 68 lines · 64 tokens per session scan A d5db82ea74bb
apply-goodharts-law is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 23d ago), licensed MIT. It adds 64 tokens to every session and 1,704 once invoked, about $0.0003 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 2026-09-03.
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