execution-discipline

A set of rules for carrying out long, multi-tool coding-agent sessions without wasting work or losing track of progress. It covers waiting for background tasks, handling failed checks, assigning subagents, saving results, and closing each work round cleanly.

In plain words
What is it for?
Managing long jobs, background agents, external research, failed or partial checks, large reading tasks, and sessions that need progress records or recovery.
Why use it?
It reduces repeated polling, uncontrolled research, unnecessary intervention, context growth, and unfinished tasks. It treats failures as signals to diagnose rather than results to pass back without follow-up.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/ooooooooooooooooooop/agent-tools/execution-discipline
Any agent
npx skills add ooooooooooooooooooop/agent-tools --skill execution-discipline
Clone the repo
git clone --depth 1 https://github.com/ooooooooooooooooooop/agent-tools

Made for: Claude Code, Codex.

Per session 199 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 9,857 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00199 $0.09857
Opus 5 $0.00100 $0.04928
Sonnet 5 $0.00040 $0.01971
Haiku 4.5 $0.00020 $0.00986

Measured 2d ago against content hash ef9aefd53d44, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

execution-discipline 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 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/flow_check.py, scripts/takeover_check.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/execution-discipline/SKILL.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

执行纪律(Execution Discipline)

本 skill 由一次真实长程会话审计(session-<id>)提炼而成。那次会话 13 小时、72 轮、12 次压缩、30 次无 wait 轮询、外部研究惊群空转约 90 分钟、多次把决策抛回用户,最终在 goal 8/8 耗尽时仍未完成。这些错误全部有现成规则可依,缺的是执行时把它们当硬门禁。 本 skill 把十条铁律固化为"执行点自查",任何新会话在匹配场景下加载后必须逐条对照。

十条铁律

铁律一:等通知,不轮询(零轮询)

  • 子代理 / 后台任务 / CLI 请求完成时,系统会主动推送完成通知,无需查询。
  • 派发后本轮立即收尾结束,等通知进入下一轮。
  • 确需确认请求存在时,list_agents / job_list / request_status / get_cli_requests 只允许调用一次;确认后转入单次长轮询:job_output(job_id, wait=true, timeout_ms=60000)request_result(request_id, wait_seconds=60~120)wait_task_receipt(receipt_path, wait_seconds=60~120)wait_supervisor_event(...)
  • 长轮询超时返回 ≠ 有事件wait_supervisor_event 180s 超时后直接再次 wait(since_seq 推进),禁止在两次 wait 之间夹 get_managed_claude_supervisor / list_managed_claude_supervisors 查询——WAIT→GET 夹用就是轮询变体(2026-08-24 曾连续 44 分钟)。
  • 收到"重复相同工具调用"系统警告 = 必须立即改变策略:先读上次结果,换参数/换工具/换方案,绝不在同一调用上再试。
  • 等待期必须并行推进,禁止纯等(2026-08-26 沉淀):长轮询/等 CLI 结果期间,先列出"不依赖该结果的待办"并逐个执行(读产物、查未闭合项、推进其他 todo),再回到轮询;同一回合内连续 ≥2 次 request_result/job_output 之间没有其他工具调用 = 空转信号。来源实证:2026-08-26 论文会话 Turn 7 的 16 次调用中 9 次输出 <200 tokens 的纯轮询,38 分钟内 Top1000 归属等可并行项未推进。

铁律二:门禁结果是诊断信号,不是交付物(自主闭环)

  • INVALID / FAIL / PARTIAL 意味着"继续修",不是"可以交差了"。
  • 收到门禁拒绝后先回答三个问题再决定下一步:根因在哪(数据/构造/门禁本身)?修哪里能过?需要什么证据证明修好了?
  • 禁止向用户输出"是否继续?/你怎么看 A/B/C?/待决策"这类抛回;只有存在确实不可自行裁决的方向分歧时才问,且一次问完。
  • 措辞从"待决策"改为"我将继续做 X,因为 Y"。
  • 用户写死的门禁(如"B/C 失败则停止")不允许在执行端悄悄重定义语义;确需调整,必须显式向用户说明理由并获批。

遇阻自救三连(检查设计 → 联网搜索 → 真正推进)

接上一条:遇到任何问题、失败、门禁拒绝或"似乎无法继续"时,不要记录阻塞或抛回用户,默认按以下顺序自动推进,不添加人工环节:

  1. 先检查设计:追调用链、查门槛/终态,用证据判定是"实现问题"还是"机制缺口"(grep 入口、读源码、跑最小复现)。
  2. 再联网搜索:内置 web_search 不可用则走 CLI worker(queue_cli_request,显式 target_model+effort,极窄探针,结构化回收),对照业界做法。
  3. 然后真正推进:把诊断变成系统层改动(代码/门禁/机制)+ 回归测试 + 验证生效,而不是写报告或加文档规则。

三连做完前禁止记 blocked/BLOCKED;只有无可用工具、无替代路径、无新增证据且达到预算才算确认阻塞。难度/不确定性不是阻塞理由。

三连是 Recovery Ladder 的快速版;完整恢复路径、失败分类与停止裁决见下方「Recovery Ladder 与合法停止策略」(canonical,其他 Skill 的停止/恢复规则不得比它更宽松)。

自主执行契约(Autonomy Execution Contract,canonical)

Read the full file on GitHub · 285 lines

Files

What ships with it

4 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. 2d ago First seen · 285 lines · 199 tokens per session scan A ef9aefd53d44

Subscribe to this mod's changes

execution-discipline is a skill published in the GitHub repository ooooooooooooooooooop/agent-tools (2 stars, last pushed 3d ago), licensed MIT. It adds 199 tokens to every session and 9,857 once invoked, about $0.0010 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 2026-08-31.

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