dynamic-workflow-mode
Lets your agent create adaptive workflows. It sets up checks and extracts reusable skills. Useful for building flexible and efficient systems.
What it does
This tool helps create flexible automation systems for coding tasks. It allows AI agents to build temporary 'harnesses' (sets of instructions) for specific tasks or extract reusable parts for future use, making teamwork more organized and observable.
When to use it
- ✓When you need a repetitive process that might change.
- ✓When creating automations for one-off tasks that need tracking.
- ✓When multiple people need to use the same process consistently.
Example of what to ask your AI
“Create a harness to test new code features, with focused tests and coverage checks.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI defines a clear plan with what needs to be done and what won't be done. It also specifies the necessary inputs (like files or data) and the expected outputs (like reports or code).
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill dynamic-workflow-modeOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/dynamic-workflow-mode/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
- One-shot task: keep it inline. Do not invent a harness.
- Repeated task with changing inputs: create a task-local harness and keep it under a temp or project-local working area.
- Repeated task across teammates or repos: extract the pattern into a shared skill.
- Task with external state, queueing, or approvals: add control pane visibility before adding more automation.
- 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 Type | Eval Gate |
|---|---|
| Code feature | Focused test, lint, coverage, and one integration path |
| UI/control pane | Browser smoke with screenshot and overflow/error checks |
| Agent workflow | Fixture transcript or seeded work item with expected routing |
| Research/content | Source-neutral brief, claim checklist, and publish-ready outline |
| Integration | Dry-run command, config validation, and no-secret scan |
…
FAQ
What is a 'harness'?
Think of a 'harness' as a set of instructions or a mini-system that guides the AI to perform a specific task, ensuring it follows the correct steps and checks the results.
What's the difference between a temporary 'harness' and a reusable 'skill'?
A temporary 'harness' is made for a single task or a small set of similar tasks. A reusable 'skill' is a more robust process that can be used by many people across different projects.
Why are 'eval gates' (evaluation checkpoints) important?
'Eval gates' are quality and safety checks. They ensure a task is completed correctly before moving on or finishing, preventing errors and ensuring the result is reliable.
Details
Original description by the author
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.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #178
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
accessibility
Helps your agent create interfaces that everyone can use. Ensures websites and apps work well for people with disabilities, like those who are blind or have reduced mobility.
affaan-m/ECC
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC
Similar skills
git-workflow-and-versioning
Organizes code work using Git. Helps save progress, manage different versions, and avoid conflicts when working in a team.
addyosmani/agent-skills
observability-and-instrumentation
Adds tools to monitor what the code does in production. Helps understand if something went wrong and why, making fixes easier.
addyosmani/agent-skills
github-pr-workflow
Prepares GitHub pull requests, ensuring code quality and documentation.
paperclipai/paperclip