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 feedback-loopsgit 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/feedback-loops)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/feedback-loops"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/feedback-loops/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/feedback-loops"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/feedback-loops.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 | $0.00142 | $0.02152 |
| Opus 5 | $0.00071 | $0.01076 |
| Sonnet 5 | $0.00028 | $0.00430 |
| Haiku 4.5 | $0.00014 | $0.00215 |
Grade B, and why
feedback-loops scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- **Coach mode:** unfamiliar or no concrete case → guide, don't lecture. How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feedback Loops
Overview
A system has a feedback loop when its output circles back as input to the next cycle. Reinforcing loops amplify (compound interest, viral growth, bank runs, death spirals). Balancing loops self-correct (thermostats, price discovery, immune response). The critical complication is delay: when delay is long relative to response time, even well-designed balancing loops produce oscillation and overshoot — and operators systematically mismanage the system (Sterman 1989: supply-line underweight = 0.34 on a 0–1 scale).
Composes with: second-order-thinking · s-curve-technology-adoption · prisoners-dilemma · probabilistic-thinking
When to Use
Apply when: system shows non-linear surprise (collapse, oscillation, death spiral, growth flywheel); you are intervening in a complex system and success depends on how it responds; trends are not extrapolating well; bullwhip or oscillation in any quantity that should be steady; a capex/AI-adoption flywheel is compounding and you need to know when the balancing limits (power, supply, cost, AI-native competition) will bite and whether it will overshoot.
When NOT to use: one-shot linear decision with no feedback; insufficient data to map loops (hand-waving without structure); decision too time-bounded for delays to matter; exogenous shock dominates internal dynamics.
Coaching Novices (Adaptive Front Door)
- Engine mode: concrete case → run The Process directly.
- Coach mode: unfamiliar or no concrete case → guide, don't lecture.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line what-it-is: when a system's output circles back as input, you have a feedback loop — it self-amplifies (reinforcing) or self-corrects (balancing), and delays make behavior far worse than expected.
- Check fit against When to Use / When NOT to use. If it's a one-shot linear decision, redirect.
- Elicit their real case: a specific behavior or dynamic they face right now — not a hypothetical.
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input — map the loop, classify it, locate the delay.
[WAIT — do not advance until user responds]
- Close by naming the leverage point uncovered and why it is higher than a parameter fix.
[WAIT — do not advance until user responds]
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 · 119 lines · 142 tokens per session scan B c954cf9c81c3
feedback-loops is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 142 tokens to every session and 2,152 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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