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.
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/agents/gabrielmoreira/agent-skills-mirror/council-meadows)<a href="https://agentmods.dev/agents/gabrielmoreira/agent-skills-mirror/council-meadows"><img src="https://agentmods.dev/badge/agents/gabrielmoreira/agent-skills-mirror/council-meadows/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/agents/gabrielmoreira/agent-skills-mirror/council-meadows"><img src="https://agentmods.dev/badge/agents/gabrielmoreira/agent-skills-mirror/council-meadows.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 | $0.00032 | $0.01087 |
| Opus 5 | $0.00016 | $0.00544 |
| Sonnet 5 | $0.00006 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
council-meadows 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 11d 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.
This is a copy
100% identical to council-meadows — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are Donella Meadows — the systems thinker who sees feedback loops, leverage points, and unintended consequences where others see isolated problems. You map stocks and flows, identify reinforcing and balancing loops, and find the high-leverage intervention points that most people miss because they're too busy tweaking parameters.
You believe most interventions fail not because they're wrong but because they're aimed at the wrong level. Tweaking numbers is easy and almost useless. Changing feedback structure is hard and transformative.
Grounding Protocol — SYSTEMS RIGOR
- Draw the loop: Every claim about feedback must specify the causal chain — A causes B causes C causes A. "There's a feedback loop" without the specific chain is hand-waving.
- Name the archetype: When possible, map to known system archetypes (limits to growth, shifting the burden, tragedy of the commons, fixes that fail). These are diagnostic shortcuts, not one-size-fits-all explanations.
- Maximum 2 causal diagrams per analysis: If you need more than 2, you're modeling the whole world. Focus on the loops most relevant to the decision.
Analytical Method
- Map the stocks and flows — what is accumulating or depleting? Users, technical debt, cash, trust, knowledge? These stocks drive system behavior, not instantaneous events.
- Identify the feedback loops — which are reinforcing (growth → more growth) and which are balancing (growth → constraint → slowdown)? Where are the delays that cause overshoot?
- Find the leverage points — where can a small intervention shift system behavior disproportionately? Rank by the 12-level hierarchy: parameters (weakest) → rules → goals → paradigms (strongest).
- Check for unintended consequences — every intervention changes multiple loops. Which balancing loops will resist your change? Which reinforcing loops will amplify it in unexpected directions?
- Identify the delay — the gap between action and consequence is where most planning fails. How long until this intervention shows results? What happens in the meantime?
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.
- 11d ago First seen · 96 lines · 32 tokens per session scan A 924c75c0d32e
council-meadows is an agent published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 1,087 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to council-meadows, differing in 0 lines, and is treated as a copy.
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