跳到正文
cccskills
免费在 GitHub 公开

agent-ops-focus-scan

Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(2)

  • SKILL.md6.2 KB
  • metadata.json1.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Focus Scan

Purpose

Align session focus with the highest priority pending issue. Enforces that all work is tracked as issues and respects confidence-based batch limits.

Context Optimization

Read index.md first for quick issue overview. Only load full priority files when selecting specific issues to work on.

1. Read .agent/issues/index.md (compact summary)
2. Identify target priority level from index
3. Load only that priority file for full details

CLI Commands

Works with or without aoc CLI installed. Focus scanning can be done via direct file reading.

File-Based Priority Scan (Default)

1. Read .agent/issues/index.md (compact summary)
2. Identify target priority level from index
3. Load only that priority file for full details
4. Filter for status: todo or open
5. Select first actionable issue (respecting depends_on)

Focus Update (File-Based)

  1. Read current .agent/focus.md
  2. Update "Doing now" section with selected issue
  3. Update "Next" section with immediate step

CLI Integration (when aoc is available)

When aoc CLI is detected in .agent/tools.json, these commands provide convenience shortcuts:

CommandPurpose
aoc issues list --status todo,open --priority criticalFind critical actionable issues
aoc issues list --status todo,open --priority highFind high priority issues
aoc issues list --status blockedFind blocked issues
aoc issues show <ID>Check issue details
aoc issues summaryQuick status overview

Confidence-Based Batch Limits

ConfidenceMax Issues per IterationRationale
LOW1 (hard limit)High uncertainty requires focused human oversight
NORMALUp to 3Standard workflow with reasonable batching
HIGHUp to 5Well-understood work can be batched

Enforcement:

  • Read confidence from constitution or current task
  • If LOW confidence, select EXACTLY 1 issue — no batching
  • Present batch to user for confirmation if confidence is not HIGH

Issue-First Enforcement

Before starting any work:

  1. Check for existing issue: Is there an issue for the requested work?

    • Yes → proceed to priority scan
    • No → prompt to create issue first
  2. If user requests work without an issue:

    ⚠️ No issue found for this work.
    
    All work should be tracked for auditability.
    
    Create an issue first?
    [Y]es, create and start / [N]o, work without tracking
    
    Suggested: {TYPE}-{next}@{hash} — "{inferred title}"
    
  3. Even simple chores need issues:

    • "Fix typo" → CHORE issue
    • "Update dependency" → CHORE issue
    • "Add comment" → DOCS issue
    • "Quick refactor" → REFAC issue (needs permission anyway)

Procedure

  1. Check confidence level (from constitution or task):

    • Determine batch size limit
    • Note if LOW confidence (special handling)
  2. Scan priority files in order (stop when actionable issue found):

    • .agent/issues/critical.md (scan first — highest priority)
    • .agent/issues/high.md
    • .agent/issues/medium.md
    • .agent/issues/low.md (only if nothing higher)
    • SKIP .agent/issues/backlog.md — backlog items need triage first
  3. Identify the highest priority issue where:

    • Status is todo or in_progress
    • All depends_on dependencies are done (or list is empty)
    • Issue is not marked as blocked, cancelled, or dropped
  4. Apply batch limit based on confidence:

    For LOW confidence:

    🎯 LOW CONFIDENCE MODE — Single Issue Focus
    
    Selected: {ISSUE-ID} — {title}
    
    Batch size: 1 (hard limit for low confidence)
    
    This issue will receive:
    - Mandatory interview before planning
    - Extensive implementation details
    - Hard stop after implementation for human review
    - ≥90% test coverage requirement
    
    Proceed with this issue? [Y]es / [N]o, select different
    

    For NORMAL confidence:

    📋 Selected {N} issues for this iteration (max 3):
    
    1. {ISSUE-ID-1} — {title}
    2. {ISSUE-ID-2} — {title}
    
    Proceed? [Y]es / [M]odify selection / [S]ingle issue only
    

    For HIGH confidence:

    • Proceed with selection (up to 5), report only
  5. Action based on findings:

    If an actionable issue is found:

    • Read .agent/focus.md
    • Update Doing now to reflect this issue (include issue ID)
    • Update Next with the immediate practical step
    • Example:
      ## Doing now
      - Working on FEAT-0012@a3f2c1: Add user authentication
      
      ## Next
      - Review existing auth patterns in codebase
      - Create implementation plan
      

    If all issues are blocked:

    • Identify the specific blockers
    • Update focus to indicate blockers need resolution
    • Suggest resolving dependencies or creating new issues

    If no prioritized issues exist but backlog has items:

    • Prompt user:
      📋 No prioritized issues, but {N} items in backlog.
      
      1. Triage backlog (/agent-task triage)
      2. Create new issue (/agent-task)
      3. Run discovery (/agent-baseline, /agent-review)
      

    If no issues exist at all:

    • Update focus to indicate planning/discovery needed
    • Prompt user:
      📋 No issues anywhere. What would you like to do?
      
      1. Create new issues (/agent-task)
      2. Run discovery to find work (/agent-baseline, /agent-review)
      3. Describe what you want to work on (I'll create an issue)
      

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

2agent

免费

Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

add-agent

免费

Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

Add new Agent templates to the meta-agent-skills framework.

中文简介正在准备中。当前显示原文说明。

diegosouzapw/awesome-omni-skill642026年3月2日 更新

查看 diegosouzapw 的全部技能

报告此技能的问题