refine-task

A task-review guide for turning an unclear Agiflow task into a specific, testable work request. Agiflow is the task-management system this guide refers to.

In plain words
What is it for?
Reviewing task details, asking focused questions, drafting clearer requirements, and saving approved changes.
Why use it?
It reduces guesswork about the intended result, boundaries, acceptance checks, dependencies, priority, and ownership.

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/hashgraph-online/awesome-codex-plugins/refine-task
Any agent
npx skills add hashgraph-online/awesome-codex-plugins --skill refine-task
Clone the repo
git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins

Made for: Claude Code, Codex.

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 403 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 $0.00052 $0.00403
Opus 5 $0.00026 $0.00201
Sonnet 5 $0.00010 $0.00081
Haiku 4.5 $0.00005 $0.00040

Measured yesterday against content hash 5656f367407a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

refine-task 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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

plugins/AgiFlow/ai-plugin/skills/refine-task/SKILL.md · 42 lines

What it actually says

Agiflow Refine Task

Make the selected task ready for confident execution while preserving its original intent.

Workflow

  1. Resolve the task with get_current_scope, list_projects, list_tasks, or get_task as needed.
  2. Call get_task for full details and list_task_comments when prior decisions may affect scope.
  3. Call list_project_statuses and list_members only when status or assignment context is relevant.
  4. Evaluate the task for:
    • Clear user or business outcome
    • Concrete scope boundaries
    • Objective acceptance criteria
    • Known dependencies and blockers
    • Appropriate priority and assignee
    • Enough context to complete without guessing
  5. Ask focused clarification questions for unresolved decisions. Do not invent requirements.
  6. Draft the refined title, description, acceptance criteria, scope boundaries, and dependency notes.
  7. Show the proposed changes and request approval.
  8. After approval, call update_task with only the fields that need to change.
  9. Call get_task again to verify the saved result.

Quality Test

Acceptance criteria must be specific, measurable, achievable within the task, relevant to its outcome, and directly verifiable. Replace phrases such as "works correctly" or "handles errors" with observable behavior.

Guardrails

  • Do not add new product requirements during refinement.
  • Do not move the task from Planning to Todo. Backlog grooming owns that transition.
  • Do not delete the task.
  • Preserve useful existing context and comments.

Response

Summarize what changed, which ambiguities were resolved, and whether the task is ready for backlog-grooming.

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. yesterday First seen · 42 lines · 52 tokens per session scan A 5656f367407a

Subscribe to this mod's changes

refine-task is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (859 stars, last pushed 3d ago), licensed Apache-2.0. It adds 52 tokens to every session and 403 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.

Related

Other skills, from other repositories

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.

taishi-i/awesome-ChatGPT-repositories · 57 tokens

jira-cli

Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.

Code-and-Sorts/awesome-copilot-agents · 60 tokens

agentflow

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

sickn33/agentic-awesome-skills · 52 tokens

sprr

Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".

wilsonfreitas/awesome-quant · 60 tokens

bprr

Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.

wilsonfreitas/awesome-quant · 61 tokens

check-mcp-json

Safely review, triage, repair, and merge ToolSDK MCP Registry package JSON pull requests. Use when an agent needs to validate files under packages/, detect duplicate registry keys, classify community PRs, make authorized fixes on contributor branches, close invalid or duplicate PRs, or squash-merge approved PRs.

toolsdk-ai/toolsdk-mcp-registry · 68 tokens