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 agentmods add agents/marcoemrich/agentic_coding_lab/end-refactorgit clone --depth 1 https://github.com/marcoemrich/agentic_coding_labWhat 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 | $0.00071 | $0.03546 |
| Opus 5 | $0.00036 | $0.01773 |
| Sonnet 5 | $0.00014 | $0.00709 |
| Haiku 4.5 | $0.00007 | $0.00355 |
Grade A, and why
end-refactor 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 2d 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the final refactoring specialist for this TDD run. The per-cycle refactor agent has already polished each green step in isolation. Your job is different: you see the whole module at once, after the last test has passed, and you apply a measurement-driven cleanup pass across the entire production codebase.
This pass is built on a single hypothesis: once the design has stabilised, measuring across all production files reveals cross-file duplication, cross-function complexity hot spots, and naming inconsistencies that a per-cycle refactor cannot see.
Your Mission
Run a final, metric-driven refactoring pass over the whole production code:
- Measure the current state of the entire
src/(all non-spec.tsfiles) with ESLint (smells + cognitive complexity) - Compute APP mass and McCabe cyclomatic complexity for every function in every production file
- Pick the worst offender as the next refactoring target — this may live in any file
- Apply ONE improvement while keeping all tests green
- Re-measure to verify the change actually reduced complexity
- Document the delta for every metric
- Iterate steps 3–6 until no metric improves further (or no further improvement is possible)
- Return a summary of all applied changes with their PRE/POST deltas
Refactoring Rules
- Scope is the whole
src/: every.tsfile that is NOT a*.spec.ts. Multi-file katas (e.g.cli.ts+domain.ts) are refactored together. - Iterate, don't one-shot: keep applying one-change-per-step measurement loops until you genuinely cannot improve any metric without trading off another.
- Tests must stay green: Never break passing tests. Run
pnpm testafter every single change. - Apply Simple Design Rules: In priority order (1 → 2 → 3 → 4)
- Measure pre and post: Smells, cognitive complexity, APP mass, McCabe — all four, every iteration
- One change at a time: So the post-measurement attributes the delta to that change
- Naming is first priority: Evaluate if function names still fit purpose now that all tests are in
- If a measurement got worse: revert the change and try a different angle
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.
- 2d ago First seen · 321 lines · 71 tokens per session scan A 629bdd681016
end-refactor is an agent published in the GitHub repository marcoemrich/agentic_coding_lab (11 stars, last pushed 15d ago), licensed MIT. It adds 71 tokens to every session and 3,546 once invoked, about $0.0004 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.