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Agent skill

Engineering Loop

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Implement a repository code or configuration change through reproduction, focused checks, review, and evidence.

Apache-2.0Auto-check passedMarketing & SEO

Install Engineering Loop

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill engineering-loop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins engineering-loop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/Phelan164/codex-howto/skills/engineering-loop .claude/skills/engineering-loop && rm -rf skills-src

Use ~/.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/

Facts

Skill name
engineering-loop
GitHub stars
1.2k
Token cost
~1.2k tokens
SKILL.md length
588 words
Files
7 (incl. references)
Skills in repo
736
Repo updated
First seen
Licence
Apache-2.0

At a glance

Implement a repository code or configuration change through reproduction, focused checks, review, and evidence.

  • Works in 5 steps: Read applicable AGENTS.md files and… → Record the requested behavior,… → Inspect the branch and working tree;… → …
  • End-to-end features and fixes
  • SKILL.md covers Establish the baseline, Run the loop, Stop conditions and Finish with evidence
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Engineering Loop is an agent skill from hashgraph-online/awesome-codex-plugins. Implement a repository code or configuration change through reproduction, focused checks, review, and evidence. Use for end-to-end features and fixes; not for explanation-only, review-only, research, content publishing, or production operations.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `agents/openai.yaml`, `references/evidence-example.md` and `references/hard-debugging.md`).

It sits in Marketing & SEO, covering Social publishing and cross-posting. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • End-to-end features and fixes
  • Not for explanation-only
  • Content publishing
  • Production operations

Example prompts

  • “/engineering-loop”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Read applicable AGENTS.md files and repository documentation.
  2. Record the requested behavior, constraints, and observable done conditions.
  3. Inspect the branch and working tree; preserve unrelated user-owned changes.
  4. Before changing shared behavior, trace affected callers, consumers, and tests
  5. Identify repository-native validation and run the smallest safe baseline

What it can do on your machine

Read from SKILL.md and the folder at commit 16b4156. 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

Engineering Loop loads about 1.2k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 588 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

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 hashgraph-online/awesome-codex-plugins at commit 16b4156, republished under its Apache-2.0 licence (© hashgraph-online). 588 words, ~1,187 tokens.

Download SKILL.mdSave it as .claude/skills/engineering-loop/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
engineering-loop
description
Implement a repository code or configuration change through reproduction, focused checks, review, and evidence. Use for end-to-end features and fixes; not for explanation-only, review-only, research, content publishing, or production operations.

Engineering Loop

Deliver a verified change with minimal supervision and proportionate checking. Start with one agent. Delegate only when authorized and independent work or review coverage justifies the overhead. Follow applicable repository and team requirements; this skill adds no ticketing or mandatory-agent process.

Establish the baseline

  1. Read applicable AGENTS.md files and repository documentation.
  2. Record the requested behavior, constraints, and observable done conditions.
  3. Inspect the branch and working tree; preserve unrelated user-owned changes.
  4. Before changing shared behavior, trace affected callers, consumers, and tests using targeted search or an available code graph. Expand along relevant paths.
  5. Identify repository-native validation and run the smallest safe baseline that separates pre-existing failures from task regressions.

Resolve routine choices from evidence. Ask only for missing authority or a material decision: ambiguous acceptance, incompatible public behavior, or destructive/data-changing side effects not already authorized. Continue independent authorized work while awaiting an answer.

Load only the needed reference:

Run the loop

  1. Reproduce the defect, or capture the current behavior for a feature.
  2. Choose the smallest coherent change and the evidence that will prove it.
  3. Add a failing regression test first when practical.
  4. Implement one bounded change and run the narrowest relevant check.
  5. If an unplanned path is necessary, record why and update the scope and checks.
  6. Classify failures as product, test, environment, assumption, or external blocker. Do not change product code to mask environment failures.
  7. Run affected consumer checks and repository-required broader checks.
  8. Review the complete diff first for task-contract compliance, then for code quality, regressions, security, and maintainability.
  9. Fix consequential findings and rerun checks affected by those fixes.

Once acceptance evidence and required checks pass, broaden or repeat checks only for new changes, failures, or unresolved concerns. Add tests when they prove behavior or a meaningful invariant, not merely mirror a low-impact edit.

If no test harness exists, use a small repeatable script or documented manual check with inputs, expected behavior, and observed results. Record missing automated coverage; a build alone does not prove runtime behavior. Do not add a large testing framework solely to satisfy the loop.

Keep a compact ledger of facts, changed files, commands, outcomes, and the next decision instead of full logs.

Show full SKILL.md (192 more words)Show less

Stop conditions

  • After two identical failures, stop blind retries and reclassify the cause. Resume only with changed relevant state or an evidence-producing probe.
  • If discovery or review repeats without new evidence, checkpoint the diff, unresolved issue, and next useful probe. Do not restart the same cycle.
  • Stop and report a blocker when progress requires missing authority, secrets, unavailable infrastructure, an unauthorized destructive action, or an unresolved material product decision.
  • When an explicit loop budget is exhausted, stop with the latest judge evidence instead of silently expanding the budget. Without an explicit budget, continue only while a bounded next action can produce new evidence; productive iterations have no arbitrary fixed count.
  • Never make a failing check pass by weakening assertions, deleting coverage, hiding errors, or silently changing acceptance criteria.
  • Do not query production, deploy, migrate, merge, or publish unless the user explicitly authorizes that action.

Finish with evidence

Report changed behavior and files, regression or acceptance proof, exact check commands and outcomes, review disposition, and remaining limitations. Include the final reviewed revision when independent review was used. Never label blocked validation or unresolved consequential findings as complete. See evidence-example.md for a filled-in handoff.

© hashgraph-online, Apache-2.0. 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 6 other files (references) in plugins/Phelan164/codex-howto/skills/engineering-loop of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/openai.yaml
  • references/evidence-example.md
  • references/hard-debugging.md
  • references/independent-review.md
  • references/loop-contract.md
  • references/loop-policy.md

Open the folder on GitHubat commit 16b4156

Compare with similar skills

Engineering Loop 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.

Engineering Loop compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Engineering Loop this skillhashgraph-online/awesome-codex-plugins1.2k—~1.2kAutomated safety check: PassApache-2.0
WeChat Official Account PosterJimLiu/baoyu-skills26k2 repos~3.6kAutomated safety check: WarnMIT
Canghe Post To Wechatfreestylefly/canghe-skills4613 repos~3.4kAutomated safety check: NotesNone
Canghe Post To Xfreestylefly/canghe-skills4614 repos~1.7kAutomated safety check: WarnNone
Ops SocialsLifecycle-Innovations-Limited/claude-ops540—~3.6kAutomated safety check: NotesMIT
Post to WeiboJimLiu/baoyu-skills26k1 repos~1.4kAutomated safety check: WarnMIT

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Categories

Questions about Engineering Loop

What does Engineering Loop do?

Implement a repository code or configuration change through reproduction, focused checks, review, and evidence. Engineering Loop is an agent skill from hashgraph-online/awesome-codex-plugins. Implement a repository code or configuration change through reproduction, focused checks, review, and evidence.

When should I use Engineering Loop?

Engineering Loop fits situations like: end-to-end features and fixes; not for explanation-only; content publishing; production operations.

How do I install Engineering Loop in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill engineering-loop -a claude-code`. Or copy the skill folder (plugins/Phelan164/codex-howto/skills/engineering-loop in hashgraph-online/awesome-codex-plugins) into .claude/skills/engineering-loop in your project. Claude Code loads it when a task matches its description.

How do I install Engineering Loop in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill engineering-loop -a codex`. Or copy the skill folder (plugins/Phelan164/codex-howto/skills/engineering-loop in hashgraph-online/awesome-codex-plugins) into .agents/skills/engineering-loop in your project. Codex loads it when a task matches its description.

Can I use Engineering Loop 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 hashgraph-online/awesome-codex-plugins --skill engineering-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/engineering-loop, .gemini/skills/engineering-loop, .github/skills/engineering-loop and .opencode/skills/engineering-loop in your project.

What does Engineering Loop need to run?

SKILL.md names no scripts, command-line tools or credentials: Engineering Loop is instructions for the agent only.

Does Engineering Loop 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 Engineering Loop 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 Engineering Loop use?

Engineering Loop is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Engineering Loop use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Engineering Loop?

Skills that share tags, products or a category with Engineering Loop: WeChat Official Account Poster (JimLiu/baoyu-skills, 26k stars), Canghe Post To Wechat (freestylefly/canghe-skills, 461 stars), Canghe Post To X (freestylefly/canghe-skills, 461 stars) and Ops Socials (Lifecycle-Innovations-Limited/claude-ops, 540 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Engineering Loop?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,232 GitHub stars. The repository holds 736 skills in this directory. The repository was last updated on October 6, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.