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 deciqAI/knowledge-skills --skill red-queen-effectgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/red-queen-effect)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/red-queen-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/red-queen-effect/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/deciqai/knowledge-skills/red-queen-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/red-queen-effect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00115 | $0.02025 |
| Opus 5 | $0.00057 | $0.01012 |
| Sonnet 5 | $0.00023 | $0.00405 |
| Haiku 4.5 | $0.00012 | $0.00202 |
Grade A, and why
red-queen-effect 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.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red Queen Effect
Overview
The Red Queen Effect: competitors must continuously improve just to maintain relative position — because everyone else is improving simultaneously. Absolute performance rises; relative position barely shifts; cumulative effort primarily produces consumer surplus, not corporate profit. Named after Leigh Van Valen's 1973 evolutionary law and Lewis Carroll's Red Queen ("It takes all the running you can do, to keep in the same place").
Composes with porters-five-forces (diagnoses structure; Red Queen explains why strong competitors still don't earn), second-curve (escaping the Red Queen is the primary case for a second curve), network-effects (temporary escape until the next technology generation resets the field), and antifragile (gaining from Red Queen stress rather than merely surviving it).
When to Use
- Investing heavily but market share is not moving; industry growing but margins chronically thin
- Competitive gap stays constant despite continuous product improvement
- Post-mortem: advantage was copied within 12–24 months; team keeps asking "should we match them?"
- Evaluating whether to enter an industry or whether an initiative will produce durable advantage
- Escalating AI capex / compute arms race, or AI-native competition where everyone must adopt AI just to keep pace and no durable lead emerges
Not when: genuine structural barriers to imitation exist (IP, regulatory approvals, deep network effects); pure operational efficiency decision; pre-competitive with no direct rivals. Stop: once Red Queen is confirmed + escape vector identified, or NOT confirmed + durability factor named.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific competitive situation → run The Process directly.
- Coaching mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 126 lines · 115 tokens per session scan A 818791a479ad
red-queen-effect is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 115 tokens to every session and 2,025 once invoked, about $0.0006 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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