tether-ai: Skill for Claude Code

.agents/skills/continue-long-run/SKILL.md

continue-long-run is a skill for Claude Code, Codex from tt-11-dd/tether-ai. It costs 40 tokens per session (242 once invoked), scanned A, original, MIT.

A continuation guide for repositories that already have a .agents/features.json task list. It works on one unfinished item at a time and marks it complete only after verification passes.

In plain words
What is it for?
Use it to resume a saved coding task, complete the next checklist item, and verify each item before recording success.
Why use it?
It prevents a multi-session task from skipping unfinished work or marking changes complete without checking them.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is tt-11-dd/tether-ai's own configuration. It tells Claude Code and Codex how to work on tether-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything tether-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to tt-11-dd/tether-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/tt-11-dd/tether-ai/main/.agents/skills/continue-long-run/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/tt-11-dd/tether-ai

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 continue-long-run

README.md
[![agentmods](https://agentmods.dev/badge/skills/tt-11-dd/tether-ai/continue-long-run/github.svg)](https://agentmods.dev/skills/tt-11-dd/tether-ai/continue-long-run)
Your own site
<a href="https://agentmods.dev/skills/tt-11-dd/tether-ai/continue-long-run"><img src="https://agentmods.dev/badge/skills/tt-11-dd/tether-ai/continue-long-run/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 continue-long-run

Your own site · 80×15
<a href="https://agentmods.dev/skills/tt-11-dd/tether-ai/continue-long-run"><img src="https://agentmods.dev/badge/skills/tt-11-dd/tether-ai/continue-long-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 242 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 $0.00040 $0.00242
Opus 5 $0.00020 $0.00121
Sonnet 5 $0.00008 $0.00048
Haiku 4.5 $0.00004 $0.00024

Measured 9d ago against content hash 42c43ed71857, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

continue-long-run 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 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.

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.

.agents/skills/continue-long-run/SKILL.md · 14 lines

What it actually says

继续跨会话任务

  1. pwd。读 .agents/progress.md.agents/features.json、最近 git log
  2. 只挑一条 passes: false 的最高优先级项。不要同时开几条。
  3. 做完后跑仓库里已有的检查(本仓库是 pnpm testpnpm typecheck;其它仓库用它 README / package.json 里的测试命令)。失败则修,不准改 passes
  4. 验收通过才把该项 passes 改为 true。只许改 passes,不要删条目、不要改 description
  5. .agents/progress.md 末尾追加:做了哪条、改了哪些文件、下一条是什么。
  6. 有 git 则提交,说明完成了哪条 feature。
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. 9d ago First seen · 14 lines · 40 tokens per session scan A 42c43ed71857

Subscribe to this mod's changes

continue-long-run is a skill published in the GitHub repository tt-11-dd/tether-ai (82 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 242 once invoked, about $0.0002 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 2026-08-30.

Related

Other skills, from other repositories

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

romiluz13/auto-pi · 37 tokens

memory-compounding

Review and sharpen persistent memory so it compounds instead of accumulating. Use when pruning pi-hermes-memory entries, doing monthly memory hygiene, or when the same lesson has been recorded multiple times.

romiluz13/auto-pi · 41 tokens

bearings

Generate a "pick up where I left off" status report across all active Pi sessions. Use when the user asks for bearings, a status report, morning brief, catch-up, "where did I leave off", or "what's in the works". Reads live session state, composes a scannable 4-section digest, and writes a dated report to…

romiluz13/auto-pi · 84 tokens

effect-rpc-cluster

Build typed RPC endpoints and cluster-distributed entities, singletons, cron jobs, and durable workflows with Effect's RPC and Cluster modules (Rpc/RpcGroup/RpcServer/RpcClient, Entity/Sharding/Singleton, Node/Bun bundles). Use when building RPC services or distributed/clustered Effect systems.

mpsuesser/pi-effect-harness · 68 tokens

effect-http-api

Build typed HTTP APIs with Effect's HttpApi — endpoints with schemas, handlers, security middleware, OpenAPI docs, derived clients, and handler unit tests. Use when building HTTP servers, REST APIs, or typed HTTP clients with Effect v4.

mpsuesser/pi-effect-harness · 53 tokens

effect-error-handling

Implement typed error handling in Effect v4 using Schema.TaggedErrorClass, catchTag/catchTags, catchReason/catchReasons, Cause, ErrorReporter, and recovery patterns. Use this skill when working with Effect error channels, handling expected failures, or designing error recovery strategies.

mpsuesser/pi-effect-harness · 61 tokens