Borrowing it
Nothing to install: this file belongs to flowing-abyss/obsidian-hybrid-search. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/flowing-abyss/obsidian-hybrid-search/master/.ai/skills/executing-plans/SKILL.mdgit clone --depth 1 https://github.com/flowing-abyss/obsidian-hybrid-searchWrote 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/flowing-abyss/obsidian-hybrid-search/executing-plans)<a href="https://agentmods.dev/skills/flowing-abyss/obsidian-hybrid-search/executing-plans"><img src="https://agentmods.dev/badge/skills/flowing-abyss/obsidian-hybrid-search/executing-plans/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/flowing-abyss/obsidian-hybrid-search/executing-plans"><img src="https://agentmods.dev/badge/skills/flowing-abyss/obsidian-hybrid-search/executing-plans.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 | $0.00035 | $0.04987 |
| Opus 5.5 | $0.00014 | $0.01995 |
| Sonnet 5.5 | $0.00007 | $0.00997 |
| Haiku 4.5 | $0.00003 | $0.00499 |
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 today.
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.
Copies of this mod
3 near-identical copies found in the catalogue:
- executing-plans — 89% identical, 133 lines differ
- executing-plans — 89% identical, 133 lines differ
- executing-plans — 89% identical, 175 lines differ
How it starts
The opening of the file, as written. The whole thing — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executing Plans
Execute the plan yourself, task by task, in this session: no implementer subagent per task, no reviewer per task. One fresh-context review of the whole branch at the end.
Why inline: Subagent-driven development pays for a fresh implementer and a fresh reviewer on every task, each re-reading the codebase from zero. Inline execution pays for one context (yours) plus one reviewer at the end. What it gives up is a fresh context per task and a second pair of eyes per task. This skill keeps what those two things bought, by other means: the brief is the spec, the ledger is your memory, TDD is the per-task gate, and the final reviewer is the second pair of eyes.
Core principle: The plan already did the thinking. Execute it exactly, prove each step with a test you watched fail and then pass, and leave a record that survives your own forgetting.
Narration: between tool calls, narrate at most one short line , the ledger and the tool results carry the record.
Continuous execution: Do not pause to check in with your human partner between tasks. They chose inline execution to spend less, not to answer "should I continue?" after every task. Execute all tasks from the plan without stopping.
Rulings, not stalls. Conflicts, ambiguities, plan defects , decide them.
The spec is the binding authority, the plan is its argument, and your
judgment settles what neither answers. Record every decision in the ledger
as Ruling: <what you decided> , <why> , <what it costs if wrong>, and keep
going. Deviating from the plan without a ledgered ruling is a decision made
in secret.
Four things stop you, and only these: an irreversible or destructive operation; a security-sensitive action; a side effect outside this worktree that norms say you ask about first (a merge, a push to a shared branch, a publish); and a plan so broken that every path forward is a guess. For those, stop and ask.
When to Use
- You have a plan from superpowers:writing-plans and your human partner chose inline execution at the handoff.
- Your harness has no subagent tool (see the per-platform references in
../using-superpowers/references/). Never fabricate a dispatch; run the plan here. - Tasks are mostly independent , the same precondition as superpowers:subagent-driven-development.
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.
- today First seen · 377 lines · 35 tokens per session scan A c6e7cd0d9c5b
executing-plans is a skill published in the GitHub repository flowing-abyss/obsidian-hybrid-search (113 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 4,987 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 2026-10-24.
Other skills, from other repositories
tdd
A test-first development workflow based on TDD, or test-driven development. It writes a failing test, adds code to make it pass, and then improves the code without changing its behavior.
test-driven-development
(TDD) Use when implementing any feature or bugfix. Apply the Logic Gate to identify what needs tests, then use the Iron Rule for strict test-first development on logic.
test-driven-development
A software-development method called test-driven development, or TDD, where you write a test first, confirm that it fails, then write the smallest implementation that makes it pass.
using-superpowers
An engineering workflow that guides an agent from idea clarification through design, planning, test-driven development, review, verification, and branch cleanup. Test-driven development, or TDD, means writing a failing test before the production code that makes it pass.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
engram-testing-coverage
TDD and coverage standards for Engram. Trigger: When implementing behavior changes in any package.