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 skills/bengous/claude-code-plugins/t-plannpx skills add bengous/claude-code-plugins --skill t-plangit clone --depth 1 https://github.com/bengous/claude-code-pluginsWhat 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 | $0.00048 | $0.02515 |
| Opus 5 | $0.00024 | $0.01257 |
| Sonnet 5 | $0.00010 | $0.00503 |
| Haiku 4.5 | $0.00005 | $0.00251 |
Grade A, and why
t-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 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T-Plan Skill
You are the orchestrator here, not the implementer. Exploration, alternatives research, and documentation validation each belong to a subagent, so the plan is assembled from independent findings rather than from one context that already decided the answer.
Start by creating the session directory (mkdir -p .t-plan/<session-id>), writing
intent.md to capture the request, and creating the master task with TaskCreate. The
artifacts are the point — the user invoked /t-plan for the full orchestrated workflow, not
ad-hoc help.
Transform conversations into rock-solid implementation plans, coordinating subagents with
the Agent tool and tracking their work with the Task* tools.
Architecture Overview
No hooks required - Agent dispatch plus the Task* tools handle all coordination natively.
Orchestrator responsibilities:
- Session initialization (mkdir, .gitignore, current.txt pointer)
- Task lifecycle (TaskCreate, TaskUpdate)
- Pre-truncation before subagent dispatch
- Output verification after subagent return
- Retry logic (max 2 attempts)
Subagent responsibilities:
- Read context files (intent.md, explore.md, etc.)
- Write output files directly (explore.md, scout.md, validation-vNNN.json)
- Focus on their assigned task
Reference material (load when needed):
- Retry logic & parallel execution: See
references/subagent-patterns.md - Planning principles & anti-patterns: See
references/planning-principles.md - Plan output template: See
references/plan-template.md
Workflow
INTENT -> EXPLORE -> [gate] -> SCOUT -> DRAFT -> VALIDATE -> PLAN
| | | | | |
orch. subagent subagent orch. subagent orch.+user
| Step | Actor | Output |
|---|---|---|
| INTENT | Orchestrator | Clear intent (gate: can direct EXPLORE?) |
| EXPLORE | Subagent | Codebase insights (gate: trivial -> skip?) |
| SCOUT | Subagent (optional) | Alternatives only (no docs) |
| DRAFT | Orchestrator | Initial approach (reads files, synthesizes) |
| VALIDATE | Subagent (required checkpoint) | Doc validation + snippets |
| PLAN | Orchestrator + User | Final plan, iterate until approved |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 336 lines · 48 tokens per session scan A a3e6f6533ead
t-plan is a skill published in the GitHub repository bengous/claude-code-plugins (4 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 2,515 once invoked, about $0.0002 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.
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html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
verify-security
安全校验关卡。自动扫描代码安全漏洞，检测危险模式，确保安全决策有文档记录。当用户提到安全扫描、漏洞检测、安全审计、代码安全、OWASP、注入检测、敏感信息泄露时使用。在新建模块、安全相关变更、攻防任务、重构完成时自动触发。.
development
开发语言能力索引。Python、Go、Rust、TypeScript、Java、C++、Shell。当用户提到编程、开发、代码、语言时路由到此。.
post-build-flow
Handles workflow verification and setup after build-workflow succeeds, or when the message contains workflow-verification-follow-up or workflow-setup-required. Load after direct builds, when verificationReadiness requires action, or on orchestrator verify/setup follow-up turns.
n8n:human-like-code-review
Reviews a GitHub pull request like a thoughtful human reviewer and writes the feedback to a markdown file. Prioritizes context, architecture fit, solution complexity, bugs, security edge cases, and missing tests. Use when given a PR URL to review, or when the user says /human-like-code-review.