Best fit
- Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses.
affaan-m/ECC
Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses.
npx skills add https://github.com/affaan-m/ECC --skill "skills/dynamic-workflow-mode"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: 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, an…
npx skills add https://github.com/affaan-m/ECC --skill "skills/dynamic-workflow-mode"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
Objective: - Ship: - Do not ship:
The user mentions dynamic workflows, custom harnesses, harness-per-task, adaptive workflows, or Claude Code dynamic workflow mode.
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:
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…
Use this structure before writing code:
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use dynamic-workflow-mode to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned dynamic-workflow-mode source to [task]. Pay particular attention to these source sections: “Dynamic Workflow Harness”, “When To Activate”, “Core Contract”, “Dynamic Harness Decision Tree”, “Task-Local Harness Template”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current dynamic-workflow-mode result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “Dynamic Workflow Harness” has been checked.
The source section “When To Activate” has been checked.
The source section “Core Contract” has been checked.
The source section “Dynamic Harness Decision Tree” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.
Open source detailUse when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "
A separate implementation from MoizIbnYousaf/marketing-cli; compare its source, maintenance signals, and permission requirements.
Open source detailHandles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
A separate implementation from github/awesome-copilot; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
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, an…
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/dynamic-workflow-mode". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
Use when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.
Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.
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.
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:
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:
Promote a task-local harness into a shared skill only when at least two of these are true:
When extracting, write the skill first in skills/<name>/SKILL.md. Add command shims only if a legacy slash-entry surface is still required.
Dynamic workflow mode becomes team-usable when it exposes state. Record these checkpoints whenever the task spans more than one session:
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.
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 |
Do not claim a dynamic workflow is reusable until the eval can be rerun by another teammate.
Finish with: