apply-earnings-myopia-defense

apply-earnings-myopia-defense is a skill for Claude Code from jeffreytse/grimoire-core. It costs 87 tokens per session (2,252 once invoked), scanned A, original, MIT.

A decision framework for protecting long-term investments from pressure to improve the next quarterly earnings report. It applies to spending such as research, infrastructure, and maintenance.

In plain words
What is it for?
It helps review investment proposals, challenge short-term budget decisions, and protect long-term work during financial planning.
Why use it?
It helps prevent short-term reporting goals from causing cuts or delays to projects that create lasting value.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the grimoire-business plugin — 145 skills shipped together

Good fit It helps review investment proposals, challenge short-term budget decisions, and protect long-term work during financial planning.

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Install with agentmods
npx agentmods add skills/jeffreytse/grimoire-core/apply-earnings-myopia-defense
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 jeffreytse/grimoire-core --skill apply-earnings-myopia-defense
Clone the repo
git clone --depth 1 https://github.com/jeffreytse/grimoire-core

Made for: Claude Code.

Or install grimoire-business, the plugin that ships this one along with the rest of its 145 skills.

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.

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README.md
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Your own site
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-earnings-myopia-defense"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-earnings-myopia-defense/github.svg" alt="Measured on agentmods" height="20"></a>

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Your own site · 80×15
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-earnings-myopia-defense"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-earnings-myopia-defense.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,252 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 $0.00087 $0.02252
Opus 5 $0.00044 $0.01126
Sonnet 5 $0.00017 $0.00450
Haiku 4.5 $0.00009 $0.00225

Measured 9d ago against content hash 2f2a44e3fc22, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

apply-earnings-myopia-defense 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 9d 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/business/strategy/skills/apply-earnings-myopia-defense/SKILL.md · 67 lines

How it starts

The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Apply Earnings Myopia Defense

Before letting quarterly-earnings pressure cut or delay a genuinely positive-long-term-value investment — R&D, infrastructure, maintenance — recognize this as a specific, well-documented, measurable cause of real value destruction, and build structural protections that keep long-term investment decisions insulated from short-term reporting pressure.

Why This Is Best Practice

Why best: This is not a vague complaint that "companies think too short-term" — Graham, Harvey & Rajgopal's research specifically measured how often financial executives would sacrifice a project with genuinely positive long-term value specifically to hit a quarterly earnings target, and found this trade-off is common, deliberate, and widely acknowledged by the executives making it, not an accidental byproduct of poor planning. Because the mechanism is specific and measurable (earnings-target pressure causing a deliberate cut to positive-NPV long-term investment), the defense is also specific: structurally reduce or eliminate the short-term reporting pressure that creates the incentive to make this trade-off, rather than relying on individual managers to resist it through willpower alone.

Graham, Harvey & Rajgopal (2005): This widely cited survey of over 400 financial executives found a striking, explicit result: a majority of surveyed CFOs stated they would decrease discretionary spending on R&D, advertising, and maintenance — even when they believed such spending had genuine long-term positive value — specifically to meet a quarterly earnings benchmark. The survey also found executives placed a high priority on meeting earnings benchmarks (matching the prior year's earnings, meeting analyst consensus, and reporting smooth, low-volatility earnings) even above maximizing the firm's actual long-term value, and were explicit that this reflected real, deliberate trade-offs rather than an oversight.

Bushee (1998): Provided empirical, firm-level evidence for the same mechanism from the opposite direction — measuring actual R&D spending against a firm's institutional ownership composition, Bushee found that firms with a higher proportion of "transient" institutional investors (those trading on short-term earnings performance rather than holding for long-term value) were significantly more likely to cut R&D spending specifically to avoid missing short-term earnings expectations, compared to otherwise similar firms with more long-term-oriented ownership — directly linking the composition of a company's investor base to the severity of this specific underinvestment pattern.

Read the full file on GitHub · 67 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. 9d ago First seen · 67 lines · 87 tokens per session scan A 2f2a44e3fc22

Subscribe to this mod's changes

apply-earnings-myopia-defense is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 24d ago), licensed MIT. It adds 87 tokens to every session and 2,252 once invoked, about $0.0004 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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