Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mattmre/EVOKORE-MCP-PUBLIC --skill master-workflow-555git clone --depth 1 https://github.com/mattmre/EVOKORE-MCP-PUBLICWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mattmre/evokore-mcp-public/master-workflow-555)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/master-workflow-555"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/master-workflow-555/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/master-workflow-555"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/master-workflow-555.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.01165 |
| Opus 5 | $0.00029 | $0.00583 |
| Sonnet 5 | $0.00012 | $0.00233 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
master-workflow-555 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Master Workflow 5/5/5
The 5/5/5 master workflow is the highest-level coding-task template in EVOKORE. It walks one task through 5 phases, with 5 panel reviews invoked at the critical decision points, and 5 quality gates that must pass before the next phase starts.
When to use it
Reach for this workflow when:
- The task is non-trivial (would benefit from explicit planning + verification rather than ad-hoc edits).
- You want panel-of-experts review at the critical decision points.
- You need a single, auditable trail of phases → panels → gates → deliver.
- You want a structured DAG without losing autonomous-loop continuity (no new blocking surfaces — gates are conditional, not interactive).
Do not use it for trivial single-edit tasks; the overhead is wasted there.
The 5/5/5 structure
| # | Phase | Panel | Gate |
|---|---|---|---|
| 1 | Plan — produce an implementation plan | Architecture Planning | Plan approved |
| 2 | Explore — survey the relevant codebase | Feasibility Research | Exploration complete |
| 3 | Implement — apply the plan | Code Refinement | Implementation complete |
| 4 | Verify — run tests, fill coverage gaps | Testing Quality | Tests green |
| 5 | Handoff — emit PR body + session-log update | Documentation Quality | Handoff ready |
Each gate is a conditional step that evaluates a simple expression on the outputs of its phase (and, where applicable, its panel). A gate that does not pass short-circuits the rest of the DAG — but the workflow still runs deliver so the operator gets a partial report instead of a black box.
Inputs
| Input | Type | Required | Default | Notes |
|---|---|---|---|---|
task_title |
string | yes | Short imperative title | |
task_brief |
string | yes | Self-contained task brief | |
artifacts |
array | no | [] |
Initial known files / URLs |
skip_panels |
array | no | [] |
Panel IDs to skip (e.g. ["architecture"]) |
max_phase_seconds |
number | no | 1800 |
Per-phase timeout |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 109 lines · 58 tokens per session scan A 4a3feeac4c68
master-workflow-555 is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 1,165 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
flow-nexus-swarm
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform.
new-gh-issue-orchestration
Orchestrates a GitHub-issue-driven delivery workflow from issue intake to PR creation using reviewer-first then worker execution. Invoked when the user provides a GitHub issue link/number and asks to start end-to-end delivery.
accint-solve
Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.
mpm-workflow
Manage and customize MPM workflow configurations with local overrides.
Subagent Driven Development
Orchestrate specialized, autonomous AI agents to execute parallel subtasks with strict boundaries.