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/sarkarsaurabh27/agent-loop-learningWrote 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/rules/sarkarsaurabh27/agent-loop-learning/improve-agent)<a href="https://agentmods.dev/rules/sarkarsaurabh27/agent-loop-learning/improve-agent"><img src="https://agentmods.dev/badge/rules/sarkarsaurabh27/agent-loop-learning/improve-agent/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/rules/sarkarsaurabh27/agent-loop-learning/improve-agent"><img src="https://agentmods.dev/badge/rules/sarkarsaurabh27/agent-loop-learning/improve-agent.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 | $0.00041 | $0.00592 |
| Opus 5.5 | $0.00016 | $0.00237 |
| Sonnet 5.5 | $0.00008 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
improve-agent 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 1mo 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.
What it actually says
Improve Agent Component
When the user asks to improve an agent or a specific component:
- Explore the current repo first — do not ask the user for anything yet. Search for agent-related code:
- Files matching
*agent*,*tool*,*prompt*,*chain*,*workflow*insrc/,lib/,app/, root - Imports of agent frameworks:
langchain,langgraph,openai,anthropic,autogen,crewai - System prompt definitions, tool definitions, agent loop logic
CLAUDE.md,AGENTS.md, or any README describing the agent architecture Read the relevant files. Only ask the user if no agent code is found after exploring.
- Files matching
Then map their keyword to the relevant best-practice doc:
| Keyword | Doc to read |
|---|---|
| orchestration / multi-agent | best-practices/01-multi-agent-orchestration.md |
| worker / scaffold | best-practices/02-worker-prompting.md |
| prompting / prompt | best-practices/02-worker-prompting.md + 07-prompt-engineering.md |
| memory / context / rag | best-practices/03-context-and-memory.md |
| tools / tool | best-practices/04-tool-design.md |
| verification / testing / test | best-practices/05-verification-and-testing.md |
| security / permissions / injection | best-practices/06-security-and-permissions.md |
| performance / startup / latency | best-practices/08-performance-and-startup.md |
| (no keyword / full system) | all 9 docs |
Read the matched doc(s), then produce improvement cards using this format:
### Improvement Plan: [component or "Full System"]
**Context** (1–2 sentences on current state)
---
**[Improvement title]**
- Current state: ...
- Recommended change: ...
- Benchmark justification: "[number] — [source, date]"
- Implementation sketch: (pseudocode or pattern, framework-agnostic)
- Effort: Low / Medium / High
---
#### Quick wins
1–3 changes under 1 hour with high impact.
Rules:
- Prefer the simplest change that closes the gap.
- Do not recommend adding frameworks unless directly needed.
- If agent uses a specific model (GPT, Gemini, Llama), note model-specific vs. universal recommendations.
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.
- 1mo ago First seen · 58 lines · 41 tokens per session scan A 832e42deb20f
improve-agent is a cursor rule published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 592 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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