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 mrzhangguoguo/oh-my-workbuddy --skill plangit clone --depth 1 https://github.com/mrzhangguoguo/oh-my-workbuddyWrote 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/mrzhangguoguo/oh-my-workbuddy/plan)<a href="https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/plan"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/plan/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/mrzhangguoguo/oh-my-workbuddy/plan"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/plan.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 | $0.00075 | $0.03350 |
| Opus 5 | $0.00037 | $0.01675 |
| Sonnet 5 | $0.00015 | $0.00670 |
| Haiku 4.5 | $0.00007 | $0.00335 |
Grade A, and why
plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ported from oh-my-codex
plan. OMX runtime conventions ($macroinvocation,omxCLI,.omx/state directory) are replaced with WorkBuddy idioms (Skill tool, Agent tool, task list,.workbuddy/memory).
Plan Skill
Plan creates comprehensive, actionable work plans through structured interaction. It auto-detects whether to interview the user (broad requests) or plan directly (detailed requests), and supports consensus mode (iterative Planner/Architect/Critic loop with RALPLAN-DR structured deliberation) and review mode (Critic evaluation of existing plans).
Use When
- User wants to plan before implementing — "plan this", "let's plan".
- User wants structured requirements gathering for a vague idea.
- User wants an existing plan reviewed — "review this plan",
--review. - User wants multi-perspective consensus —
--consensus, "ralplan". - Task is broad or vague and needs scoping before code.
Do Not Use When
- User wants autonomous end-to-end execution — use an execution workflow instead.
- User wants to start coding immediately on a clear task — just do it.
- User asks a simple question answerable directly.
- Task is a single focused fix with obvious scope.
Why This Exists
Jumping into code without understanding requirements leads to rework and missed edge cases. Plan provides structured requirements gathering, expert analysis, and quality-gated plans so execution starts from a solid foundation. Consensus mode adds multi-perspective validation for high-stakes work.
Execution Policy
- Auto-detect interview vs direct mode based on request specificity.
- Ask one question at a time during interviews — never batch multiple interview rounds into one form.
- Gather codebase facts via the Explore subagent (Agent tool,
subagent_type: Explore) before asking the user about them. Use normal repository inspection (Read/Grep/Glob/Bash) for read-only lookups; reserve heavier shell evidence for explicit native commands. - Plans must meet quality standards: 80%+ claims cite file/line, 90%+ criteria are testable.
- Implementation step count must be right-sized to task scope; avoid defaulting to exactly five steps.
- Consensus mode outputs the final plan by default; add
--interactiveto enable execution handoff. - Consensus mode uses RALPLAN-DR short mode by default; switch to deliberate mode with
--deliberateor when the request explicitly signals high risk (auth/security, data migration, destructive/irreversible changes, production incident, compliance/PII, public API breakage). - Apply the shared workflow guidance pattern: outcome-first framing, concise visible updates for multi-step planning, local overrides for the active branch, evidence-backed planning, explicit stop rules, and automatic continuation for safe reversible steps. Ask only for material, destructive, credentialed, external-production, or preference-dependent branches.
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 · 173 lines · 75 tokens per session scan A e07eb9ae3340
plan is a skill published in the GitHub repository mrzhangguoguo/oh-my-workbuddy (2 stars, last pushed 2mo ago), licensed MIT. It adds 75 tokens to every session and 3,350 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 2026-08-31.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…