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dynamic-workflow-mode

Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses. Use when building a task-local harness, adding eval gates, or extracting a reusable skill from ad-hoc work.

O que é dynamic-workflow-mode?

dynamic-workflow-mode is a Claude Code agent skill that design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses. Use when building a task-local harness, adding eval gates, or extracting a reusable skill from ad-hoc work.

Funciona comClaude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/dynamic-workflow-mode

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Documentação

Dynamic Workflow Mode

Use this skill when a coding agent can generate or adapt a task-local harness instead of only following a static command flow. The goal is to turn dynamic workflow mode into a disciplined system: temporary harnesses for one-off work, shared skill extraction for repeated work, and observable control pane checkpoints for teams.

When To Activate

  • The user mentions dynamic workflows, custom harnesses, harness-per-task, adaptive workflows, or Claude Code dynamic workflow mode.
  • A task needs a custom loop, evaluator, crawler, fixture generator, watcher, or local dashboard.
  • Multiple agents need the same repeatable process but the process is not yet captured as a shared skill.
  • A workflow needs durable handoff artifacts, eval evidence, or operator approval before merge.

Core Contract

Dynamic workflow mode should produce a task-local harness only when the harness is cheaper and safer than manually driving the same steps. The harness must have:

  • Objective: the outcome it owns and the outcome it explicitly does not own.
  • Inputs: files, URLs, prompts, data sources, credentials policy, and user-provided constraints.
  • Outputs: commits, reports, screenshots, status files, or control pane snapshots.
  • Eval: at least one pass/fail check tied to the task, not only "it ran".
  • Handoff: a short artifact that tells the next operator what happened, what is blocked, and how to resume.

Dynamic Harness Decision Tree

  1. One-shot task: keep it inline. Do not invent a harness.
  2. Repeated task with changing inputs: create a task-local harness and keep it under a temp or project-local working area.
  3. Repeated task across teammates or repos: extract the pattern into a shared skill.
  4. Task with external state, queueing, or approvals: add control pane visibility before adding more automation.
  5. Task with safety risk: add an eval gate and a human merge gate before autonomous execution.

Task-Local Harness Template

Use this structure before writing code:

# Dynamic Workflow Harness

Objective:
- Ship:
- Do not ship:

Inputs:
- Repo or workspace:
- External systems:
- Credentials policy:

Loop:
1. Discover current state.
2. Generate or update the smallest useful artifact.
3. Run eval checks.
4. Record status and handoff.
5. Stop on failed gate, unclear ownership, or unsafe external action.

Eval:
- Command:
- Expected pass signal:
- Failure owner:

Handoff:
- Status:
- Evidence:
- Next action:

Shared Skill Extraction

Promote a task-local harness into a shared skill only when at least two of these are true:

  • The same workflow appears in multiple sessions, repos, teams, or launches.
  • The workflow needs specific language, tool, or safety sequencing.
  • Failures repeat because operators skip a gate or lose context.
  • The workflow has a stable input/output contract.
  • The workflow benefits from a control pane, status board, or team handoff.

When extracting, write the skill first in skills/<name>/SKILL.md. Add command shims only if a legacy slash-entry surface is still required.

Control Pane Checkpoints

Dynamic workflow mode becomes team-usable when it exposes state. Record these checkpoints whenever the task spans more than one session:

  • Plan: objective, owner, acceptance criteria, and risky external systems.
  • Queue: work items, assigned agent role, branch/worktree, and dependency edges.
  • Run: active harness, current loop step, recent eval result, and token/cost signal if available.
  • Gate: test results, browser screenshots, security review, and merge readiness.
  • Handoff: what is done, what failed, what needs a human decision.

If the repo has ECC2 state enabled, prefer adding or reading checkpoints through the ECC control pane or state-store-backed scripts instead of scattering untracked notes.

Eval Gates

Every dynamic harness needs a task-specific eval. Pick the cheapest reliable gate:

Work TypeEval Gate
Code featureFocused test, lint, coverage, and one integration path
UI/control paneBrowser smoke with screenshot and overflow/error checks
Agent workflowFixture transcript or seeded work item with expected routing
Research/contentSource-neutral brief, claim checklist, and publish-ready outline
IntegrationDry-run command, config validation, and no-secret scan

Do not claim a dynamic workflow is reusable until the eval can be rerun by another teammate.

Anti-Patterns

  • Generating scripts that hide the real decision logic from the operator.
  • Treating dynamic workflow mode as permission to skip tests.
  • Creating one-off docs when a shared skill or status artifact is the real product.
  • Running multiple agents without ownership, merge gate, or conflict policy.
  • Letting raw private research data leak into public docs.

Output Standard

Finish with:

  • The harness or skill path.
  • The eval commands and results.
  • The control pane or handoff artifact path.
  • The next reusable extraction candidate.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

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affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

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affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

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affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

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agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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