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/rileyhilliard/agentrc/executing-plansnpx skills add rileyhilliard/agentrc --skill executing-plansgit clone --depth 1 https://github.com/rileyhilliard/agentrcWhat 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 | $0.00028 | $0.01284 |
| Opus 5 | $0.00014 | $0.00642 |
| Sonnet 5 | $0.00006 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
executing-plans 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executing Plans
You are an orchestrator. Spawn and coordinate sub-agents to do the actual implementation. Group related tasks by subsystem (e.g., one agent for API routes, another for tests) rather than spawning per-task. Each agent re-investigates the codebase, so fewer agents with broader scope = faster execution.
1. Setup
Create a branch for the work unless trivial. Consider git worktrees for isolated environments.
Clarify ambiguity upfront: If the plan has unclear requirements or meaningful tradeoffs, use AskUserQuestion before starting. Present options with descriptions explaining the tradeoffs. Use multiSelect: true for independent features that can be combined; use single-select for mutually exclusive choices. Don't guess when the user can clarify in 10 seconds.
Track progress with tasks: Use TaskCreate to create tasks for each major work item from the plan. Update status with TaskUpdate as work progresses (in_progress when starting, completed when done). This makes execution visible to the user and persists across context compactions.
2. Group Tasks by Subsystem
Group related tasks to share agent context. One agent per subsystem, groups run in parallel.
Why grouping matters:
Without: Task 1 (auth/login) → Agent 1 [explores auth/]
Task 2 (auth/logout) → Agent 2 [explores auth/ again]
With: Tasks 1-2 (auth/*) → Agent 1 [explores once, executes both]
| Signal | Group together |
|---|---|
| Same directory prefix | src/auth/* tasks |
| Same domain/feature | Auth tasks, billing tasks |
| Plan sections | Tasks under same ## heading |
Limits: 3-4 tasks max per group. Split if larger.
Parallel: Groups touch different subsystems
Group A: src/auth/* ─┬─ parallel
Group B: src/billing/* ─┘
Sequential: Groups have dependencies
Group A: Create shared types → Group B: Use those types
3. Execute
Dispatch sub-agents to complete task groups. Monitor progress and handle issues.
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 · 126 lines · 28 tokens per session scan A 8a38c9f9c8b3
executing-plans is a skill published in the GitHub repository rileyhilliard/agentrc (3 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 1,284 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 2026-08-31.
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