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 orch-researchgit 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/orch-research)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/orch-research"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/orch-research/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/orch-research"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/orch-research.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 | $0.00021 | $0.01259 |
| Opus 5 | $0.00010 | $0.00629 |
| Sonnet 5 | $0.00004 | $0.00252 |
| Haiku 4.5 | $0.00002 | $0.00126 |
Grade A, and why
orch-research 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 8d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
orch-research — Topic-Based Repo Research with Expert Panel Review
Purpose
Given a user-supplied narrative describing a topic, domain, or need, this command researches top GitHub repositories, evaluates candidates through expert panels, and produces a curated shortlist with integration recommendations and a presentation-ready summary.
Usage
orch-research "<narrative>" [options]
Options
| Option | Description | Default |
|---|---|---|
--max <n> |
Maximum repos to deeply evaluate | 10 |
--depth <level> |
Evaluation depth: quick, standard, deep | standard |
--context "<text>" |
Integration context (target system description) | — |
--meta-improve |
Run meta-improvement cycle after completion | false |
--no-presentation |
Skip presentation packaging | false |
Examples
Basic Topic Research
orch-research "TypeScript MCP server implementations with tool discovery and session management"
Research with Integration Context
orch-research "real-time collaboration frameworks with CRDT support" \
--context "EVOKORE-MCP v3.1 TypeScript MCP server with WebSocket support" \
--depth deep
Broad Domain Discovery
orch-research "AI agent orchestration frameworks that support multi-agent coordination, task decomposition, and parallel execution" \
--max 15
Research with Meta-Improvement
orch-research "developer productivity CLI tools with hook systems" \
--meta-improve
Reverse Engineering Portfolio Research
orch-research "recently updated reverse engineering repos with strong debugger, decompiler, automation, and malware triage workflows" \
--context "EVOKORE-MCP reverse-engineering operating model buildout" \
--depth deep \
--meta-improve
Workflow Steps
- Narrative Analysis — Parse the user's description into structured search criteria, requirements, and quality signals
- Repo Discovery (parallel)
- GitHub API search (code, repos, topics)
- Community scan (awesome-lists, blog posts, npm/pypi trending, discussions)
- Initial Filtering — Deduplicate, rank, and filter to top N candidates based on activity, license, language match, community signal
- Deep Evaluation (parallel) — For each candidate: README analysis, file tree structure, architecture pattern, test coverage, dependency health, documentation quality, unique capabilities
- Expert Panel Review — Repo Ingestion Panel (Kim, Petrov, Obi, Larsson, Tanaka) evaluates all candidates. Per-repo verdicts: ADOPT / CONSIDER / SKIP
- Feasibility Gate — Feasibility Panel (Torres, Wright, Okonkwo, Svensson) assesses integration effort, ROI, adoption approach
- Presentation Packaging — Presentation Panel extracts key findings into stakeholder-ready format
- Meta-Improvement (optional) — Evaluates expert effectiveness and workflow quality
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.
- 8d ago First seen · 143 lines · 21 tokens per session scan A 95cdfb47b7ed
orch-research is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,259 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
research-repository
Build a repository that makes findings findable, reusable, and cumulative across teams. Use when the same research keeps getting redone. For synthesising one study, use affinity-diagram.
form-design
Design a form end to end — field order, grouping, validation, and completion. Use when the artifact is a form. For product-wide error strategy use error-handling-ux; for first-run signup use onboarding-design.
peak-end-rule
Apply the Peak-End Rule — a flow is remembered by its most intense moment and its last. Use when designing completion, celebration, or cancellation moments. For sustaining engagement mid-flow, use zeigarnik-effect.
law-of-common-region
Apply the Law of Common Region — a shared container, background, or border groups elements regardless of spacing. Use when grouping must survive a tight layout. For grouping by spacing alone, use law-of-proximity.
critique-visual-hierarchy
Critique a rendered screen's hierarchy — entry point, eye flow, weight distribution, and emphasis. Use when attention lands in the wrong place. For establishing hierarchy in new work, use visual-hierarchy (ui-design).