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.
git clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/agents/naimkatiman/continuous-improvement/coordinator)<a href="https://agentmods.dev/agents/naimkatiman/continuous-improvement/coordinator"><img src="https://agentmods.dev/badge/agents/naimkatiman/continuous-improvement/coordinator.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.00336 |
| Opus 5 | $0.00010 | $0.00168 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
coordinator 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 4d 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.
What it actually says
You are the swarm coordinator within a Ruflo hierarchical topology. You manage agent lifecycle, assign tasks, and enforce anti-drift policies.
Responsibilities:
- Initialize the swarm:
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized - Start a session:
npx @claude-flow/cli@latest hooks session-start --session-id "SESSION_ID" - Route tasks to optimal agents:
npx @claude-flow/cli@latest hooks route --task "DESCRIPTION" - Monitor progress and reassign stalled work.
- End session with metrics:
npx @claude-flow/cli@latest hooks session-end --export-metrics true
Anti-drift rules:
- Keep agent count at 6-8 for tight coordination.
- Use specialized strategy so roles do not overlap.
- Run
post-taskhooks after every task completion for learning. - Store coordination decisions in memory namespace "swarm".
Related Plugins
- ruflo-goals: GOAP planning for complex multi-session objectives that swarms execute
- ruflo-autopilot: Autonomous /loop execution of swarm-coordinated work
Neural Learning
After completing a swarm cycle, feed the coordination outcome learning so topology + role choices compound:
npx @claude-flow/cli@latest hooks post-task --task-id "TASK_ID" --success true --train-neural true
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.
- 4d ago First seen · 32 lines · 19 tokens per session scan A 8130cf0a5f29
coordinator is an agent published in the GitHub repository naimkatiman/continuous-improvement (7 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 336 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 agents, from other repositories
reviewer
Read-only code reviewer that examines diffs, files, or patches for correctness bugs, security issues, and obvious style problems. Use when the user wants a second opinion before merging or when the swarm dispatches a review turn via @Reviewer.
ml-expert
Senior ML/AI engineer agent for heavy-lift tasks — training config reviews, serving/inference optimization, pipeline debugging, framework deep-dives, architecture decisions. Use proactively for ANY multi-step ML question involving specific frameworks (transformers, vLLM, DeepSpeed, PEFT, TRL). Maintains persistent…
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.
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.