agentsop-bounded-loop

agentsop-bounded-loop is a skill for Claude Code, Codex from agentsope/SkillAlchemy. It costs 218 tokens per session (7,389 once invoked), scanned A, original, MIT.

A set of rules for safely controlling workflows that repeat actions, such as plan–act–observe cycles, agent retries, or test–fix cycles. It requires an explicit stopping mechanism because a language model cannot be trusted to end a loop reliably.

In plain words
What is it for?
Use it when building or debugging multi-step agent loops, multi-agent handoffs, optimizer or evaluator cycles, or test-and-repair workflows. It applies to systems using frameworks such as LangGraph, LangChain, CrewAI, or the OpenAI Agents SDK.
Why use it?
It helps prevent runaway retries, excessive tool calls, recursion errors, and wasted resources. It also makes loop limits and stop conditions visible instead of relying on the model’s judgment alone.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for aider. Also seen: mentions Claude Code; built for aider.

Good fit Use it when building or debugging multi-step agent loops, multi-agent handoffs, optimizer or evaluator cycles, or test-and-repair workflows. It applies to systems using frameworks such as LangGraph, LangChain, CrewAI, or the OpenAI Agents SDK.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentsope/skillalchemy/agentsop-bounded-loop
Install

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.

Any agent
npx skills add agentsope/SkillAlchemy --skill agentsop-bounded-loop
Clone the repo
git clone --depth 1 https://github.com/agentsope/SkillAlchemy

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agentsop-bounded-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-bounded-loop/github.svg)](https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-bounded-loop)
Your own site
<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-bounded-loop"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-bounded-loop/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.

agentmods 80×15 button for agentsop-bounded-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-bounded-loop"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-bounded-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 218 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,389 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00218 $0.07389
Opus 5.5 $0.00087 $0.02956
Sonnet 5.5 $0.00044 $0.01478
Haiku 4.5 $0.00022 $0.00739

Measured 1mo ago against content hash 55ba9e3b096e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agentsop-bounded-loop 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.

skills/agentsop-bounded-loop/SKILL.md · 606 lines

How it starts

The opening of the file, as written. The whole thing — 606 lines — stays where its author put it; the contents beside it link to each section on GitHub.

bounded-loop · O7

Source posture: every load-bearing claim is cited inline with a short tag resolved against references/R1-source-evidence.md and references/R2-cross-framework.md. Examples cite the real GitHub issues they're distilled from.


1. 何时激活 (Activation Rules)

Activate this skill when any of the following is true:

  • The task involves a workflow that contains a cycle — tool-call → reflect → retry, plan → act → observe → re-plan, draft → critique → revise, test → fix → re-test.
  • The user is hitting a framework's "loop too deep" error: GRAPH_RECURSION_LIMIT (LangGraph), MaxIterationsExceeded (LangChain AgentExecutor), "agent exceeded max_iter" (CrewAI), max_turns reached (OpenAI Agents SDK), stop_reason="max_tokens" mid-tool-use (Anthropic).
  • The user proposes "let's just raise the limit" / "set max_iter to 100" / recursion_limit=200 — this is the canonical anti-pattern this skill exists to prevent.
  • The user is building a multi-agent system with delegation, handoff, or supervisor patterns — these are exposure-multipliers for unbounded loops (see [gh/crewai-330]).
  • The user is building an optimiser / evaluator loop (DSPy, AutoEval, RLHF, self-refining agent) where "stop when good enough" is the termination criterion — this is never sufficient on its own.
  • The user wants a test-fix loop, self-healing code agent, or iterative refinement workflow — every code-agent in production (Cursor, Aider, Devin, Claude Code) ships with an explicit step budget.

Do not activate for: single LLM calls, one-shot RAG queries, stateless tool pipelines, or flows where the cycle is provably bounded by data (e.g., "iterate once per row in this fixed list").


2. 核心心智模型 (Core Mental Model)

Every loop body must produce a state change that proves progress — and the proof must be checkable without calling another LM.

Read that twice. It contains four claims:

  1. The body must change state. A no-op iteration (same input → same output) is the definition of a stuck loop. If your body might return the same value twice, the loop is already broken; the safety net just hasn't fired yet.

Read the full file on GitHub · 606 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 606 lines · 218 tokens per session scan A 55ba9e3b096e

Subscribe to this mod's changes

agentsop-bounded-loop is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 218 tokens to every session and 7,389 once invoked, about $0.0009 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 2026-08-30.

Related

Other skills, from other repositories

shellgames

Play board games on ShellGames.ai — Chess, Poker, Ludo, Tycoon, Memory, and Spymaster. Use when the agent wants to play games against humans or other AI agents, join tournaments, chat with players, check leaderboards, or manage a ShellGames account. Triggers on "play chess/poker/ludo/memory", "shellgames", "join…

MemTensor/skills-vote · 103 tokens

curl-search

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

MemTensor/skills-vote · 55 tokens

skills-vote-local

Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.

MemTensor/skills-vote · 28 tokens

skills-vote

Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.

MemTensor/skills-vote · 44 tokens

xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or…

MemTensor/skills-vote · 201 tokens

pdf

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and…

MemTensor/skills-vote · 92 tokens