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Agent Skill · 研究检索

absolute-human(绝对的人性)

absolute-human 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

absolute-human 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 38,233。

总安装 1,591周安装 65GitHub Stars 134下载 515核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:absolute-human(绝对的人性)
来源仓库:https://github.com/absolutelyskilled/absolutelyskilled
仓库路径:skills/absolute-human
安装命令:
npx skills add https://github.com/absolutelyskilled/absolutelyskilled --skill absolute-human
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/absolutelyskilled/absolutelyskilled --skill absolute-human

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

absolute-human 提出专为 AI 代理设计的新型开发生命周期模型。适用于需要分解任务、并行执行与持续验证的 Codex、Claude、Cursor、Gemini CLI 项目。通过 GitHub 安装后启用,包含 INTAKE 到 CONVERGE 七个阶段流程。

需注意其可能涉及持久化存储与会话跟踪,确认权限边界。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/absolute-human

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/absolutelyskilled/absolutelyskilled --skill ab…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

absolute-human 怎么安装?

在终端执行 npx skills add https://github.com/absolutelyskilled/absolutelyskilled --skill absolute-hum…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

absolute-human 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

absolute-human 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

absolute-human 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

absolute-human 用的人多吗?

公开数据是累计安装 1,591 次,近一周安装 65 次,下载 515 次,GitHub 134 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

absolute-human 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
absolute-human

绝对的人性 · 本页

1,591134只读未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

When this skill is activated, always start your first response with the 🧢 emoji.

Absolute-Human: AI-Native Development Lifecycle

Absolute-Human is a development lifecycle built from the ground up for AI agents. Traditional methods like Agile, Waterfall, and TDD were designed around human constraints - limited parallelism, context switching costs, communication overhead, and meetings. AI agents have none of these constraints. Absolute-Human exploits this by decomposing work into dependency-graphed sub-tasks, executing independent tasks in parallel waves, enforcing TDD verification at every step, and tracking everything on a persistent board that survives across sessions.

The model has 7 phases: INTAKE - DECOMPOSE - DISCOVER - PLAN - EXECUTE - VERIFY - CONVERGE.


Activation Banner

At the very start of every Absolute-Human invocation, before any other output, display this ASCII art banner:

███████╗██╗   ██╗██████╗ ███████╗██████╗ ██╗  ██╗██╗   ██╗███╗   ███╗ █████╗ ███╗   ██╗
██╔════╝██║   ██║██╔══██╗██╔════╝██╔══██╗██║  ██║██║   ██║████╗ ████║██╔══██╗████╗  ██║
███████╗██║   ██║██████╔╝█████╗  ██████╔╝███████║██║   ██║██╔████╔██║███████║██╔██╗ ██║
╚════██║██║   ██║██╔═══╝ ██╔══╝  ██╔══██╗██╔══██║██║   ██║██║╚██╔╝██║██╔══██║██║╚██╗██║
███████║╚██████╔╝██║     ███████╗██║  ██║██║  ██║╚██████╔╝██║ ╚═╝ ██║██║  ██║██║ ╚████║
╚══════╝ ╚═════╝ ╚═╝     ╚══════╝╚═╝  ╚═╝╚═╝  ╚═╝ ╚═════╝ ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝  ╚═══╝

This banner is mandatory. It signals to the user that Absolute-Human mode is active.


Activation Protocol

Immediately after displaying the banner, enter plan mode before doing anything else:

  1. On platforms with native plan mode (e.g., Claude Code's EnterPlanMode, Gemini CLI's planning mode): invoke the native plan mode mechanism immediately.
  2. On platforms without native plan mode: simulate plan mode by completing all planning phases (INTAKE through PLAN) in full before making any code changes. Present the complete plan to the user for explicit approval before proceeding to EXECUTE.

This ensures that every Absolute-Human invocation begins with structured thinking. The first four phases (INTAKE, DECOMPOSE, DISCOVER, PLAN) are inherently planning work - no files should be created or modified until the user has approved the plan and execution begins in Phase 5.


Session Resume Protocol

When Absolute-Human is invoked and a .absolute-human/board.md already exists in the project root:

  1. Detect: Read the existing board and determine its status (in-progress, blocked, completed)
  2. Display: Print a compact status summary showing completed/in-progress/blocked/remaining tasks
  3. Resume: Pick up from the last incomplete wave - do NOT restart from INTAKE
  4. Reconcile: If the codebase has changed since the last session (e.g., manual edits, other commits), run a quick diff check against the board's expected state and flag any conflicts before resuming

If the board is marked completed, ask the user whether to start a new Absolute-Human session (archive the old board to .absolute-human/archive/) or review the completed work.

For intentional context resets during long-running sessions (3+ waves completed or context window pressure), see references/context-reset-protocol.md.

Never blow away an existing board without explicit user confirmation.


Codebase Convention Detection

Before INTAKE begins, automatically detect the project's conventions by scanning for key files. This grounds all subsequent phases in reality rather than assumptions.

Auto-detect Checklist

SignalFiles to Check
Package managerpackage-lock.json (npm), yarn.lock (yarn), pnpm-lock.yaml (pnpm), bun.lockb (bun), Cargo.lock (cargo), go.sum (go)
Language/Runtimetsconfig.json (TypeScript), pyproject.toml / setup.py (Python), go.mod (Go), Cargo.toml (Rust)
Test runnerjest.config.*, vitest.config.*, pytest.ini, .mocharc.*, test directory patterns
Linter/Formatter.eslintrc.*, eslint.config.*, .prettierrc.*, ruff.toml, .golangci.yml
Build systemMakefile, webpack.config.*, vite.config.*, next.config.*, turbo.json
CI/CD.github/workflows/, .gitlab-ci.yml, Jenkinsfile
Available scriptsscripts section of package.json, Makefile targets
Directory conventionssrc/, lib/, app/, tests/, __tests__/, spec/
Codedocsdocs/.codedocs.json, documentation/.codedocs.json, or any .codedocs.json in the repo

Codedocs Detection

If a .codedocs.json manifest is found, the repo has structured codedocs output. Record its location on the board and set a flag codedocs_available: true. This changes how DISCOVER and PLAN operate - see those phases for details.

When codedocs is available, read docs/OVERVIEW.md and docs/GETTING_STARTED.md immediately during convention detection and append their key facts (tech stack, module map, entry points, dev commands) to the ## Project Conventions section of the board. This front-loads context that would otherwise require separate codebase exploration in DISCOVER.

Output

Write the detected conventions to the board under a ## Project Conventions section. Reference these conventions in every subsequent phase - particularly PLAN and the Mandatory Tail Tasks verification step.


When to Use This Skill

Use Absolute-Human when:

  • Multi-step feature development touching 3+ files or components
  • User says "build this end-to-end" or "plan and execute this"
  • User says "break this into tasks" or "sprint plan this"
  • Any task requiring planning + implementation + verification
  • Greenfield projects, major refactors, or migrations
  • Complex bug fixes that span multiple systems

Do NOT use Absolute-Human when:

  • Single-file bug fixes or typo corrections
  • Quick questions or code explanations
  • Tasks the user wants to do manually with your guidance
  • Pure research or exploration tasks

Key Principles

1. Dependency-First Decomposition

Every task is a node in a directed acyclic graph (DAG), not a flat list. Dependencies between tasks are explicit. This prevents merge conflicts, ordering bugs, and wasted work.

2. Wave-Based Parallelism

Tasks at the same depth in the dependency graph form a "wave". All tasks in a wave execute simultaneously via parallel agents. Waves execute in serial order. This maximizes throughput while respecting dependencies.

3. Test-First Verification

Every sub-task writes tests before implementation. A task is only "done" when its tests pass. No exceptions for "simple" changes - tests are the proof of correctness.

4. Persistent State

All progress is tracked in .absolute-human/board.md in the project root. This file survives across sessions, enabling resume, audit, and handoff. The user chooses during INTAKE whether the board is git-tracked or gitignored.

5. Interactive Intake

Never assume. Scale questioning depth to task complexity - simple tasks get 3 questions, complex ones get 8-10. Extract requirements, constraints, and success criteria before writing a single line of code.


Core Concepts

The 7 Phases

INTAKE --> DECOMPOSE --> DISCOVER --> PLAN --> EXECUTE --> VERIFY --> CONVERGE
  |           |             |          |         |           |          |
  |  gather   |  build DAG  | research | detail  | parallel  | test +   | merge +
  |  context  |  + waves    | per task | per task| waves     | verify   | close

Task Graph

A directed acyclic graph (DAG) where each node is a sub-task and edges represent dependencies. Tasks with no unresolved dependencies can execute in parallel. See references/dependency-graph-patterns.md.

Execution Waves

Groups of independent tasks assigned to the same depth level in the DAG. Wave 1 runs first (all tasks in parallel), then Wave 2 (all tasks in parallel), and so on. See references/wave-execution.md.

Board

The .absolute-human/board.md file is the single source of truth. It contains the intake summary, task graph, wave assignments, per-task status, research notes, plans, and verification results. See references/board-format.md.

Sub-task Lifecycle

pending --> researching --> planned --> in-progress --> verifying --> done
                                           |                |
                                           +--- blocked     +--- failed (retry)

Phase 1: INTAKE (Interactive Interview)

The intake phase gathers all context needed to decompose the task. Scale depth based on complexity.

Complexity Detection

  • Simple (single component, clear scope): 3 questions
  • Medium (multi-component, some ambiguity): 5 questions
  • Complex (cross-cutting, greenfield, migration): 8-10 questions

Core Questions (always ask)

  1. Problem Statement: What exactly needs to be built or changed? What triggered this work?
  2. Success Criteria: How will we know this is done? What does "working" look like?
  3. Constraints: Are there existing patterns, libraries, or conventions we must follow?

Extended Questions (medium + complex)

  1. Existing Code: Is there related code already in the repo? Should we extend it or build fresh?
  2. Dependencies: Does this depend on external APIs, services, or other in-progress work?

Deep Questions (complex only)

  1. Edge Cases: What are the known edge cases or failure modes?
  2. Testing Strategy: Are there existing test patterns? Integration vs unit preference?
  3. Rollout: Any migration steps, feature flags, or backwards compatibility needs?
  4. Documentation: What docs need updating? API docs, README, architecture docs?
  5. Priority: Which parts are most critical? What can be deferred if needed?

Board Persistence Question (always ask)

Ask: "Should the .absolute-human/ board be git-tracked (audit trail, resume across machines) or gitignored (local working state)?"

Output

Write the intake summary to .absolute-human/board.md with all answers captured. See references/intake-playbook.md for the full question bank organized by task type.


Phase 2: DECOMPOSE (Task Graph Creation)

Break the intake into atomic sub-tasks and build the dependency graph.

Sub-task Anatomy

Each sub-task must have:

  • ID: Sequential identifier (e.g., SH-001)
  • Title: Clear, action-oriented (e.g., "Create user authentication middleware")
  • Description: 2-3 sentences on what this task does
  • Type: code | test | docs | infra | config
  • Complexity: S (< 50 lines) | M (50-200 lines) | L (200+ lines - consider splitting)
  • Dependencies: List of task IDs this depends on (e.g., [SH-001, SH-003])

Decomposition Rules

  1. Every task should be S or M complexity. If L, decompose further
  2. Test tasks are separate from implementation tasks
  3. Infrastructure/config tasks come before code that depends on them
  4. Documentation tasks depend on the code they document
  5. Aim for 5-15 sub-tasks. Fewer means under-decomposed; more means over-engineered
  6. Every task graph MUST end with three mandatory tail tasks (see below)
  7. Apply the complexity budget (see below)

Complexity Budget

After decomposition, sanity-check total scope before proceeding:

  • Count the total number of tasks by complexity: S (small), M (medium), L (large)
  • If any L tasks remain, decompose them further - L tasks are not allowed
  • If total estimated scope exceeds 15 M-equivalent tasks (where 1 L = 3 M, 1 S = 0.5 M), flag to the user that scope may be too large for a single Absolute Human session
  • Suggest splitting into multiple Absolute Human sessions with clear boundaries (e.g., "Session 1: backend API, Session 2: frontend integration")
  • The user can override and proceed, but they must explicitly acknowledge the scope

Mandatory Tail Tasks

Every task graph MUST end with three mandatory tail tasks: Self Code Review, Requirements Validation, and Full Project Verification. For detailed descriptions and acceptance criteria of each, see references/execution-patterns.md.

Build the DAG

  1. List all sub-tasks
  2. For each task, identify which other tasks must complete first
  3. Draw edges from dependencies to dependents
  4. Verify no cycles exist (it's a DAG, not a general graph)

Assign Waves

Group tasks by depth level in the DAG:

  • Wave 1: Tasks with zero dependencies (roots of the DAG)
  • Wave 2: Tasks whose dependencies are all in Wave 1
  • Wave N: Tasks whose dependencies are all in Waves 1 through N-1

Present for Approval

Generate an ASCII dependency graph and wave assignment table. Present to the user and wait for explicit approval before proceeding. See references/dependency-graph-patterns.md for common patterns, example graphs, and the wave assignment algorithm.


Phase 3: DISCOVER (Parallel Research)

Research each sub-task before planning implementation. This phase is parallelizable per wave.

Per Sub-task Research

For each sub-task, investigate in this order - docs first, source second:

  1. Codedocs Lookup (if codedocs_available: true on the board)

- Check docs/INDEX.md to find which module doc covers the files relevant to this task - Read the relevant docs/modules/<module>.md for public API, internal structure, dependencies, and implementation notes - Check docs/patterns/ for any cross-cutting pattern docs (error handling, testing strategy, logging) that apply to this task - Use docs/OVERVIEW.md for architecture context and to understand how this task's module fits into the system - Only proceed to Codebase Exploration below if the docs don't contain enough detail - flag any gaps in the docs as a staleness note on the board - Record which doc files were used in the task's research notes on the board

  1. Codebase Exploration (always run; use to fill gaps left by docs or when codedocs is not available)

- Find existing patterns, utilities, and conventions relevant to this task - Identify files that will be created or modified - Check for reusable functions, types, or components - Understand the testing patterns used in the project

  1. Web Research (when codebase context is insufficient)

- Official documentation for libraries and APIs involved - Best practices and common patterns - Known gotchas or breaking changes

  1. Risk Assessment

- Flag unknowns or ambiguities - Identify potential conflicts with other sub-tasks - Note any assumptions that need validation

Execution Strategy

  • Launch parallel Explore agents for all tasks in Wave 1 simultaneously
  • Once Wave 1 research completes, launch Wave 2 research, and so on
  • Each agent writes its findings to the board under the respective task

Output

Append research notes to each sub-task on the board:

  • Key files identified
  • Reusable code/patterns found
  • Risks and unknowns flagged
  • External docs referenced

Phase 4: PLAN (Execution Planning)

Create a detailed execution plan for each sub-task based on research findings.

Per Sub-task Plan

For each sub-task, specify:

  1. Files to Create/Modify: Exact file paths
  2. Test Files: Test file paths (TDD - these are written first)
  3. Implementation Approach: Brief description of the approach
  4. Acceptance Criteria: Specific, verifiable conditions for "done"
  5. Test Cases: List of test cases to write

- Happy path tests - Edge case tests - Error handling tests

Planning Rules

  1. Tests are always planned before implementation
  2. Each plan must reference specific reusable code found in DISCOVER
  3. Plans must respect the project's existing conventions (naming, structure, patterns)
  4. If a plan reveals a missing dependency, update the task graph (re-approve with user)

Output

Update each sub-task on the board with its execution plan. The board now contains everything an agent needs to execute the task independently.


Phase 5: EXECUTE (Wave-Based Implementation)

Execute tasks wave by wave. Within each wave, spin up parallel agents for independent tasks.

Pre-Execution Snapshot

Before executing the first wave, create a git safety net:

  1. Ensure all current changes are committed or stashed
  2. Record the current commit hash on the board under ## Rollback Point
  3. If execution goes catastrophically wrong (build broken after max retries, critical files corrupted), the user can git reset --hard to this commit
  4. Remind the user of the rollback point hash when flagging unrecoverable failures

Wave Execution Loop

for each wave in [Wave 1, Wave 2, ..., Wave N]:
  for each task in wave (in parallel):
    1. Write tests (TDD - red phase)
    2. Implement code to make tests pass (green phase)
    3. Refactor if needed (refactor phase)
    4. Update board status: in-progress -> verifying
  wait for all tasks in wave to complete
  run wave boundary checks (conflict resolution, progress report)
  proceed to next wave

For agent context handoff format, wave boundary checks (conflict resolution and progress reports), scope creep handling, blocked task management, and failure recovery patterns, see references/execution-patterns.md.


Phase 6: VERIFY (Per-Task + Integration)

Every sub-task must prove it works before closing.

Per-Task Verification

For each completed sub-task, run:

  1. Tests: Run the task's test suite - all tests must pass
  2. Lint: Run the project's linter on modified files
  3. Type Check: Run type checker if applicable (TypeScript, mypy, etc.)
  4. Build: Verify the project still builds

Integration Verification

After each wave completes:

  1. Run tests for tasks that depend on this wave's output
  2. Check for conflicts between parallel tasks (file conflicts, API mismatches)
  3. Run the full test suite if available

Generator-Evaluator Verification

Verification uses generator-evaluator separation. The agent that built the task does NOT verify it. Instead, a separate evaluator subagent grades the work against scored criteria.

  1. Signals first: Run tests, lint, type check, build. If any fail, fix before evaluator.
  2. Evaluator second: Dispatch evaluator subagent with scored rubric (Correctness, Code Quality, Completeness, Test Coverage, Integration Safety). Score 4.0+/5 to pass.
  3. Iterative refinement: Up to 5 iterations of evaluator feedback if score is 3.0-3.9 and trending upward. Escalate to user if below 3.0 or stagnant.

S-complexity tasks skip the evaluator if all signals pass. M-complexity and any failed tasks get the full evaluator.

See references/evaluator-protocol.md for the full evaluator prompt template, grading rubric, sprint contracts, and iterative refinement loop. See references/verification-framework.md for signal details.


Phase 7: CONVERGE (Final Integration)

Merge all work and close out the board.

Steps

  1. Merge: If using worktrees or branches, merge all work into the target branch
  2. Full Test Suite: Run the complete project test suite
  3. Documentation: Update any docs that were part of the task scope
  4. Summary: Generate a change summary with:

- Files created/modified (with line counts) - Tests added (with coverage if available) - Key decisions made during execution - Any deferred work or follow-ups

  1. Close Board: Mark the board as completed with a timestamp
  2. Suggest Commit: Propose a commit message summarizing the work

Board Finalization

The completed board serves as an audit trail:

  • Full history of all 7 phases
  • Every sub-task with its research, plan, and verification
  • Timeline of execution
  • Any issues encountered and how they were resolved

Gotchas

  1. Parallel agents modifying shared files without a lock strategy - Two agents in the same wave that both edit the same utility file or test fixture will produce a merge conflict at the wave boundary. During DECOMPOSE, identify shared files and assign ownership to one task; other tasks must treat those files as read-only until the owning task completes.
  2. Board marked completed but tests were never run - The mandatory tail task "Run full project verification suite" is frequently skipped when agents declare done based on subjective confidence. Never mark the board completed until the actual test/lint/build commands have been run and their output recorded on the board.
  3. DISCOVER phase skipped for "obvious" tasks - Agents assume they know the codebase well enough to skip research. Then they write code that duplicates existing utilities, uses the wrong import paths, or misses a convention that would have been visible in a 2-minute code scan. Always run DISCOVER, even for small tasks.
  4. Rollback point recorded after Wave 1 has already started - Recording the git commit hash mid-wave means the rollback point already includes partial changes. The pre-execution snapshot must be taken before any file is touched - before Wave 1 begins, not during it.
  5. Scope creep absorbed silently expands the DAG without user visibility - Agents frequently discover adjacent improvements during EXECUTE and absorb them into the current task without flagging them. This makes the wave take longer than planned, obscures what changed, and violates the scope agreement from INTAKE. Everything outside the original scope goes to the Deferred Work section.

Anti-Patterns and Common Mistakes

For the full anti-patterns table (16 patterns covering intake, decomposition, execution, scope management, and conventions), see references/execution-patterns.md.


References

For detailed guidance on specific phases, load these reference files:

  • references/intake-playbook.md - Full question bank organized by task type (feature, bug, refactor, greenfield, migration), with scaling rules and example sessions
  • references/dependency-graph-patterns.md - Common DAG patterns, ASCII rendering format, wave assignment algorithm, and example graphs
  • references/wave-execution.md - Parallel agent orchestration, agent prompt templates, blocked task handling, error recovery
  • references/verification-framework.md - TDD workflow per sub-task, verification signals, evaluator integration, failure handling
  • references/evaluator-protocol.md - Generator-evaluator separation, scored grading rubric, evaluator prompt template, sprint contracts, iterative refinement loop
  • references/context-reset-protocol.md - When and how to reset context during long sessions, handoff document format, resume procedure
  • references/board-format.md - Full .absolute-human/board.md specification with format, status transitions, and example board

Companion check

On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/.claude/skills/.agent/skills/.agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install: `` npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name> ` Skip entirely if recommended_skills` is empty or all companions are already installed.

平台分布

Codex

36.74%
按下载量换算189

Claude

31.83%
按下载量换算164

Cursor

20.29%
按下载量换算104

Gemini CLI

8.41%
按下载量换算43

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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