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 naimkatiman/continuous-improvement --skill ralplangit 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/skills/naimkatiman/continuous-improvement/ralplan)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/ralplan"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/ralplan/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/naimkatiman/continuous-improvement/ralplan"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/ralplan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 57 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 20 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.02459 |
| Opus 5.5 | $0.00008 | $0.00984 |
| Sonnet 5.5 | $0.00004 | $0.00492 |
| Haiku 4.5 | $0.00002 | $0.00246 |
Grade A, and why
ralplan 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 1mo 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralplan (Consensus Planning Alias)
Ralplan is a shorthand alias for /oh-my-claudecode:plan --consensus. It triggers iterative planning with Planner, Architect, and Critic agents until consensus is reached, with RALPLAN-DR structured deliberation (short mode by default, deliberate mode for high-risk work).
Usage
/oh-my-claudecode:ralplan "task description"
Flags
--interactive: Enables user prompts at key decision points (draft review in step 2 and final approval in step 6). Without this flag the workflow runs fully automated — Planner → Architect → Critic loop — marks the final planpending approval, outputs it, and stops without asking for confirmation or executing changes.--deliberate: Forces deliberate mode for high-risk work. Adds pre-mortem (3 scenarios) and expanded test planning (unit/integration/e2e/observability). Without this flag, deliberate mode can still auto-enable when the request explicitly signals high risk (auth/security, migrations, destructive changes, production incidents, compliance/PII, public API breakage).--architect codex: Use Codex for the Architect pass when Codex CLI is available. Otherwise, briefly note the fallback and keep the default Claude Architect review.--critic codex: Use Codex for the Critic pass when Codex CLI is available. Otherwise, briefly note the fallback and keep the default Claude Critic review.
Usage with interactive mode
/oh-my-claudecode:ralplan --interactive "task description"
Behavior
Planning/Execution Boundary
Ralplan is a planning module. It may inspect context and draft or update plan/spec/proposal artifacts, but it MUST mark those artifacts as pending approval unless the user has explicitly opted into execution in the current turn or via the structured approval UI. Before explicit execution approval, it MUST NOT run mutation-oriented shell commands, edit source files, commit, push, open PRs, invoke execution skills, or delegate implementation tasks.
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.
- 1mo ago Changed · +4 lines cf4591a6886e
- 1mo ago First seen · 137 lines · 21 tokens per session scan A 43d37c43d0d3
ralplan is a skill published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 2,459 once invoked, about $0.0001 per session on Opus 5.5. 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
opensrc
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for"…
nopus-configure
Configure nopus complexity sensitivity, extra-simple rewrites, rewrite evidence, and Pi response hiding for this user.
agentproto-llm-endpoint
Use the local LLM endpoint proxy (localhost:18090) — or your own gated public deployment — to route Claude Code / Claude SDK through custom providers (OpenRouter, Moonshot, Groq, ZAI) via an Anthropic-compatible gateway. Covers adapter-specific behaviors, auth patterns, proxy codenames, and common failure modes.
agentpush
Bridge an imported agentpush MCP server (Telegram, WhatsApp, etc.) with live agentproto sessions via the daemon's transmitter subsystem: transmitmessage sends outbound and binds the recipient to a session, inboundwatcherstart polls agentpush for new messages, and inboundendpointcreate/POST /inbound(/:slug) route…
bureau
Drive Bureau — the browser stack's installable capability server: a stealth Firefox (Camofox) + daemon that exposes browser tools as MCP-over-HTTP on :8830, plus a CLI for saved browser identities (sessions), social capture / search, adapter health probes, declarative workflows, and Guilde connection. Use when working…
ap-workflows
Run multi-stage session pipelines with agentproto workflows — workflowstart with barrier-gated stages of concurrent steps, sessionRef to reuse earlier output, workflowstatus/cancel/retry, workflowrunfile for AIP-15 WORKFLOW.md, and workflowescalationresolve for human answers. Trigger when asked to pipeline agents, fan…