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.
npx agentmods add skills/hashgraph-online/awesome-codex-plugins/run-worknpx skills add hashgraph-online/awesome-codex-plugins --skill run-workgit clone --depth 1 https://github.com/hashgraph-online/awesome-codex-pluginsWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00068 | $0.02548 |
| Opus 5 | $0.00034 | $0.01274 |
| Sonnet 5 | $0.00014 | $0.00510 |
| Haiku 4.5 | $0.00007 | $0.00255 |
Grade A, and why
run-work 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- run-work — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoked as
/agiflow:run-work. In hosts without slash-prompts, this skill is triggered by matching intent and drives AgiFlow via its MCP tools.
Usage:
/agiflow:run-work <work-unit-slug-or-id>- Execute specific work unit/agiflow:run-work- List and select from available work units
Examples:
/agiflow:run-work DXX-WU-1(using slug)/agiflow:run-work 01K8FABMNEJG1XTA9JGHSNFV40(using ID)/agiflow:run-work(interactive selection)
Guardrails
- Favor straightforward, minimal implementations first and add complexity only when it is requested or clearly required.
- Keep changes tightly scoped to the requested outcome within the work unit scope.
- A work unit represents a cohesive feature/epic that can be completed in one Claude Code session.
If a work unit slug/id is provided, load it with get_work_unit; otherwise list available work units with list_work_units for selection.
AgiFlow Project Management Guidelines
Follow the shared AgiFlow project-management guidelines in references/agiflow-agents.md — agent assignment, the task status workflow and transitions, work-unit best practices, and the tags strategy apply to this workflow.
Task Status Workflow (per task)
Each task moves individually through: Todo → In Progress → Testing → Review
The work unit stays in_progress until all tasks reach Review or Done.
If any task hits Blocked, consider setting the work unit to blocked too.
IMPORTANT: Planning Status Guard
This skill ONLY executes tasks in "Todo" or later status. Tasks in "Planning" have NOT been groomed and are NOT ready for execution. Use backlog-grooming to promote Planning tasks to Todo first.
Steps Track these steps as TODOs and complete them one by one.
1. Work Unit Selection & Loading
If work unit slug/id NOT provided:
- Use
list_work_unitsMCP tool to show available work units:- Filter by
status: "in_progress"for active work, or work units with tasks in "Todo" - Do NOT pick up work units where all tasks are still in "Planning" status
- Display: slug, title, type, priority, task count, status
- Filter by
- Ask user to select which work unit to work on.
- Once user selects, proceed with the selected work unit slug/id.
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.
- 3d ago First seen · 241 lines · 68 tokens per session scan A 9a3635049a32
run-work is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (859 stars, last pushed 5d ago), licensed Apache-2.0. It adds 68 tokens to every session and 2,548 once invoked, about $0.0003 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.
Other skills, from other repositories
run-work
Execute a work unit end-to-end: sequence tasks by dependency, implement, test between tasks, commit, and track progress. Use to deliver a complete feature in one session. Invoked as /agiflow:run-work . Uses getworkunit, listtasks, updatetask, getworkunitprogress.
search
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
git-github-flow
Use for branch prep, clean commits, PR descriptions, GitHub issue triage, changelogs, release notes, review response, merge readiness, or publishing a branch safely.
commit
Smart commit with auto-generated message based on staged changes. Use after completing a task.
Git CLI
Help with safe Git workflows (branching, rebasing, release tags) and troubleshooting.
pr-draft-summary
Create the required PR-ready summary block, branch suggestion, title, and draft description for openai-agents-python. Use before the final response whenever the current task changed runtime code, tests, examples, build/test configuration, or docs with behavior impact, regardless of perceived change size and including…