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-peter-principle-checkgit 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-peter-principle-check)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-peter-principle-check"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-peter-principle-check/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-peter-principle-check"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-peter-principle-check.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 | $0.00078 | $0.01887 |
| Opus 5 | $0.00039 | $0.00944 |
| Sonnet 5 | $0.00016 | $0.00377 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
apply-peter-principle-check 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 7d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Peter Principle Check
Before promoting someone based on strong performance in their current role, explicitly test whether the skills that made them successful there are the same skills the next level actually requires — because promotion systems that reward current-role performance alone systematically promote people into roles where those specific skills no longer apply, and this is a measured, documented organizational pattern, not just an old joke about incompetent managers.
Why This Is Best Practice
Why best: The mechanism is specific and mechanical, not a claim about individual incompetence: if promotion decisions are based primarily on demonstrated performance in the current role, and the skills required at the next level are genuinely different (for instance, individual sales performance versus managing a sales team), then the promotion criterion systematically selects for the wrong skill set at the point of promotion. Because promotion typically stops once someone is no longer excelling, organizations following this pattern accumulate people who plateau at the specific level where their old skill set stopped being what the job required — a structural consequence of the selection mechanism itself, not a claim that people generally become incompetent over time.
Peter & Hull, "The Peter Principle" (1969): The originating book formalized the observation that, in hierarchies where promotion is based on performance in the current role, employees tend to be promoted based on success at their current level until they reach a role whose requirements no longer match their demonstrated skill set — at which point they stop being promoted, having reached what the book termed their "level of incompetence." The book's core, falsifiable claim is specifically about the selection mechanism (promote based on current performance) producing this predictable pattern, not a claim that people are generally or permanently incompetent.
Benson, Li & Shue (2019): This empirical study, examining promotion decisions across a large sample of sales organizations, directly tested the Peter Principle's predictions and found firms disproportionately promoted their best current sales performers into sales management roles, despite those same performance metrics having no positive — and in some specifications a negative — relationship with subsequent success as a manager, and despite better predictors of managerial potential being available in the same data. The study also found that when firms did weigh managerial-potential indicators alongside current-role performance, promoted managers performed measurably better, providing direct causal-adjacent evidence that testing for next-level fit, not just current-role performance, materially improves promotion outcomes.
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.
- 7d ago First seen · 65 lines · 78 tokens per session scan A 8fa3159b5ffc
apply-peter-principle-check is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 22d ago), licensed MIT. It adds 78 tokens to every session and 1,887 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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