Iterative Retrieval
affaan-m/ECC
Pattern for progressively refining context retrieval to solve the subagent context problem.
用户要求持续自评、打磨到满意，或连续纠偏暴露同类质量差距时使用；整体诊断真实产物，按根因成批修复质量差距并复核，不替代验证、Review 或交付合同。
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Install Iterative Quality Convergence
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Peiiii/nextclaw iterative-quality-convergence --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .claude/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .claude/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergenceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Peiiii/nextclaw iterative-quality-convergence --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .agents/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .agents/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Peiiii/nextclaw iterative-quality-convergence --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .cursor/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .cursor/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Peiiii/nextclaw.git --path .agents/wiki/skills/process/iterative-quality-convergence--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Peiiii/nextclaw iterative-quality-convergence --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .gemini/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .gemini/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Peiiii/nextclaw iterative-quality-convergenceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .github/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .github/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Peiiii/nextclaw iterative-quality-convergence --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/wiki/skills/process/iterative-quality-convergence .opencode/skills/iterative-quality-convergence && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "iterative-quality-convergence" agent skill from https://github.com/Peiiii/nextclaw/tree/master/.agents/wiki/skills/process/iterative-quality-convergence into .opencode/skills/iterative-quality-convergence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iterative-quality-convergence", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
iterative-quality-convergence- GitHub stars
- 260
- Token cost
- ~753 tokens
- SKILL.md length
- 74 words
- Files
- 2
- Skills in repo
- 70
- Repo updated
- First seen
- Licence
- MIT
At a glance
用户要求持续自评、打磨到满意，或连续纠偏暴露同类质量差距时使用；整体诊断真实产物，按根因成批修复质量差距并复核，不替代验证、Review 或交付合同。
- Works in 5 steps: 从真实入口整体观察，对照任务目标、质量标准和适用参考，建立一份可复查基线与差距列表… → 选择当前影响最大、证据最强的差距组，明确本批要闭合的完整范围、保留的行为、验证入口… → 成批完成已确定且兼容的改动。新事实改变目标、结构或必要边界时返回正确阶段；否则不为… → …
- SKILL.md covers 定位, 进入条件, 建立质量模型 and 执行粒度与效率约束, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Iterative Quality Convergence is an agent skill from Peiiii/nextclaw. 用户要求持续自评、打磨到满意，或连续纠偏暴露同类质量差距时使用；整体诊断真实产物，按根因成批修复质量差距并复核，不替代验证、Review 或交付合同。
Its SKILL.md is about 750 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
The repository describes itself as: A human-centered long-term AI partner—not a task-centered assistant. The licence is MIT.
Example prompts
- “/iterative-quality-convergence”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 从真实入口整体观察，对照任务目标、质量标准和适用参考，建立一份可复查基线与差距列表；区分缺陷、范围内质量改进、主观偏好和新需求；新需求不得借优化扩张进入当前任务。
- 选择当前影响最大、证据最强的差距组，明确本批要闭合的完整范围、保留的行为、验证入口和尚未解决的不确定性。沿现有设计与 owner 执行，普通批次不新增独立计划或长日志。
- 成批完成已确定且兼容的改动。新事实改变目标、结构或必要边界时返回正确阶段；否则不为每个小改重新论证方案。
- 用同一真实入口复核受影响场景，确认差距缩小、原行为保留且无更大回归；验证失败按证据修正，不能用文档自评代替结果。
- 更新原任务记录中的“差距组 → 本批改动 → 变化证据 → 剩余问题”，仅为仍存在的高价值差距选择下一批。
What it can do on your machine
Read from SKILL.md and the folder at commit 4d9d500. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Iterative Quality Convergence loads about 753 tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 74 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from Peiiii/nextclaw at commit 4d9d500, republished under its MIT licence (© Peiiii). 74 words, ~753 tokens.
Download SKILL.mdSave it as
.claude/skills/iterative-quality-convergence/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- iterative-quality-convergence
- description
- 用户要求持续自评、打磨到满意，或连续纠偏暴露同类质量差距时使用；整体诊断真实产物，按根因成批修复质量差距并复核，不替代验证、Review 或交付合同。
迭代质量收敛
定位
探索“AI 能否像高标准的人类开发者一样，看到结果仍不够好时主动继续改进”。这是独立的探索性工作模式，不是新的生命周期阶段，也不以增加修改轮数为目标。
高质量不能由 AI 的主观满意证明。先建立任务特定标准，再整体观察、按根因成批改进并重新验证。优化目标是在达到相同质量门槛的前提下，缩短从当前产物到可交付结果的总时间；修改轮数、代码量与忙碌程度都不是收益。
大型、多阶段任务若已有验收合同方法产出的验收契约，直接把其中的必须项、代表性场景和阶段门作为质量模型输入；本方法不另建一套完成标准。
用户明确要求把持续优化作为独立、跨轮或跨上下文任务，且下一步需要根据每轮证据重新选择时，按项目知识治理读取项目 loop 合同与对应执行记录；设计与执行状态分离，不取代本方法或开发生命周期，普通单批收敛不创建。
进入条件
显式调用项目质量迭代宏，或说“自己评审并继续优化”“直到满意为止”“别等我逐个指出问题”等，均进入本方法。无需精确关键词：当前授权任务中，同一质量维度连续被纠偏，说明零散修补未收敛时，也应主动进入并说明目标。需要 AI 自主发现并修复多处质量差距的优化，使用本方法；目标清楚的一次局部修改直接走原流程，不增加持续循环。单纯询问观感或引用宏名称不触发实施；已有循环中的询问只补充判断，不取消原目标。
开始前确认：
- 目标、目标用户、成功条件、非目标和禁止边界；
- 当前可运行、可渲染、可读取或可比较的真实产物；
- 能证明质量变化的观察入口、参考标准或指标；
- 时间、风险或迭代预算，以及必须交给用户判断的偏好。
无法观察真实结果时不得假装收敛；先补观察条件，或明确停止。
建立质量模型
只选择当前任务最重要的三至五个维度，并为每个维度写出可观察标准。候选维度包括：
- 功能与状态行为是否正确；
- 用户任务是否顺畅、清晰且完整；
- 真实数据、边界状态和不同环境下是否仍成立；
- 信息、视觉、交互或内容是否一致并达到参考水平；
- 实现是否保持单一路径、清晰 owner 和必要复杂度；
- 结果是否真正增强预期用户价值。
不要用一个伪精确总分覆盖不同性质的差距。审美偏好、产品选择和技术正确性分别判断。
“达到顶级产品水准”需展开为当前任务的可观察标准，参考真实产品或用户提供的材料；未取得参考时说明证据边界，不宣称精确复刻或客观超越。由 AI 自设标准时保留用户原目标，不能把“精致”缩成“没有溢出”。用现有设计或任务记录保存基线、标准和当前最大差距，不另建评分系统。
执行粒度与效率约束
<!-- model-capability-patch: gap=把优化机械拆成单点微调和固定轮次，重复取证且延迟完整返工; review-on=model-change; remove-when=无此约束仍能稳定按根因成批闭合差距且质量和总耗时不退化 -->- 先整体诊断，再选择批次。 在同一次基线观察中覆盖关键质量维度与代表场景，列出高价值差距、共同根因和验证入口。证据足够后立即实施，不把已看见的问题留给下一轮才调查，也不重读未变化的事实。
- 以最小完整修复为单位，禁止默认单点微调。 按共同根因、同一责任域、依赖关系与可共同验证的范围分组；已知且兼容的改动成批闭合。最大差距决定优先级，不限制一批只能改一个点。若尺度、结构或状态模型已经错误，直接替换错误路径，不能用一串局部调参代替必要返工。
- 只有不确定性才需要小实验。 根因未明、参数相互干扰或回退代价高时，先用能区分假设的最小实验；结论明确后成批执行。不能为了少做一轮把互相冲突的方案同时实施，独立差距也不强行混成不可归因的大改。
- 验证由风险和新证据触发。 同一批共享准备、运行入口与适用检查；独立读取可批量执行。有依赖的修改、外部写入与审批保持顺序。无需每个参数改动都全量构建、逐区截图或重走全部阶段；只有结果会改变下一步决策时才设置中间检查点。批次结束复核全部受影响的必需标准，不能以提速删掉真实视觉检查、关键边界或最终 Review。
- 禁止预设优化轮数。 不承诺“做三轮”、按轮数凑进度或等待用户逐项指出下一点。每次继续都须由剩余的具体差距、未决假设或回归驱动；证据显示当前粒度或方案无效时立即重新判断，不再机械重复同类微调。
收敛执行
- 从真实入口整体观察，对照任务目标、质量标准和适用参考，建立一份可复查基线与差距列表；区分缺陷、范围内质量改进、主观偏好和新需求；新需求不得借优化扩张进入当前任务。
- 选择当前影响最大、证据最强的差距组，明确本批要闭合的完整范围、保留的行为、验证入口和尚未解决的不确定性。沿现有设计与 owner 执行，普通批次不新增独立计划或长日志。
- 成批完成已确定且兼容的改动。新事实改变目标、结构或必要边界时返回正确阶段；否则不为每个小改重新论证方案。
- 用同一真实入口复核受影响场景，确认差距缩小、原行为保留且无更大回归；验证失败按证据修正，不能用文档自评代替结果。
- 更新原任务记录中的“差距组 → 本批改动 → 变化证据 → 剩余问题”，仅为仍存在的高价值差距选择下一批。
视觉产物先检查完整画面的构图、比例、层次，再检查材质、控件与交互。使用包含本批改动的实际构建，覆盖受影响的主要尺寸、主题和关键状态；相同构建中未受影响且仍有效的证据可复用。截图必须实际打开复核，几何断言、测试通过和发布成功均不能证明审美完成。
进度说明围绕已关闭的差距、剩余风险和下一项验证，不把“第几轮”当作成果。若效率仍低，检查时间是否花在重复读取/启动/全量检查、串行处理独立工作，或缺少判断所需的关键证据；调整执行方式，不通过降低质量标准加速。没有前后可比的实测时，不宣称已提速多少倍或达到了最高速度。
停止条件
满足任一条件时停止：
- 所有必需维度达到明确标准，且没有仍值得处理的高价值差距；
- 下一轮预期收益低于实现、验证或回归成本；
- 需要用户偏好、产品方向、范围扩张或高风险授权；
- 达到约定预算，或真实环境无法继续观察。
仍有必需标准未达成且可继续处理时，不得以“边际收益不足”、首次可用或等待用户逐项点评结束。偏好需要用户选择时先完成不依赖选择的工作；真实阻塞说明缺什么及已完成什么。无预算不等于无限重做：必需标准成立、没有高价值差距后进入既有验证和交付，宏本身不追加提交、发布或外部写入授权。
最终输出质量标准、已闭合差距组与改进证据、停止原因、仍需用户判断的偏好和残余风险。不得只用“看起来不错”“测试通过”或“没有 finding”宣称达到高标准。
© Peiiii, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in .agents/wiki/skills/process/iterative-quality-convergence of Peiiii/nextclaw.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 4d9d500
Compare with similar skills
Iterative Quality Convergence next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Iterative Quality Convergence this skillPeiiii/nextclaw | 260 | — | ~753 | Automated safety check: Pass | MIT | |
| Iterative Retrievalaffaan-m/ECC | 276k | 7 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Iterative Retrievalaffaan-m/ECC | 276k | 2 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Iterative Retrievalaffaan-m/ECC | 276k | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Iterative Retrievalaffaan-m/ECC | 276k | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Convergenceparcadei/Continuous-Claude-v3 | 3.9k | 1 repos | ~316 | Automated safety check: Notes | MIT |
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Questions about Iterative Quality Convergence
What does Iterative Quality Convergence do?
用户要求持续自评、打磨到满意，或连续纠偏暴露同类质量差距时使用；整体诊断真实产物，按根因成批修复质量差距并复核，不替代验证、Review 或交付合同。. Iterative Quality Convergence is an agent skill from Peiiii/nextclaw.
How do I install Iterative Quality Convergence in Claude Code?
Run `npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a claude-code`. Or copy the skill folder (.agents/wiki/skills/process/iterative-quality-convergence in Peiiii/nextclaw) into .claude/skills/iterative-quality-convergence in your project. Claude Code loads it when a task matches its description.
How do I install Iterative Quality Convergence in Codex?
Run `npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a codex`. Or copy the skill folder (.agents/wiki/skills/process/iterative-quality-convergence in Peiiii/nextclaw) into .agents/skills/iterative-quality-convergence in your project. Codex loads it when a task matches its description.
Can I use Iterative Quality Convergence in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Peiiii/nextclaw --skill iterative-quality-convergence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iterative-quality-convergence, .gemini/skills/iterative-quality-convergence, .github/skills/iterative-quality-convergence and .opencode/skills/iterative-quality-convergence in your project.
What does Iterative Quality Convergence need to run?
SKILL.md names no scripts, command-line tools or credentials: Iterative Quality Convergence is instructions for the agent only.
Does Iterative Quality Convergence access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Iterative Quality Convergence safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Iterative Quality Convergence use?
Iterative Quality Convergence is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Iterative Quality Convergence use?
About 753 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Iterative Quality Convergence?
Skills that share tags, products or a category with Iterative Quality Convergence: Iterative Retrieval (affaan-m/ECC, 276k stars), Iterative Retrieval (affaan-m/ECC, 276k stars), Iterative Retrieval (affaan-m/ECC, 276k stars) and Iterative Retrieval (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Iterative Quality Convergence?
Peiiii (a GitHub user) maintains it in Peiiii/nextclaw, which has 260 GitHub stars. The repository holds 70 skills in this directory. The repository was last updated on October 9, 2026.
Source: Peiiii/nextclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.