Borrowing it
Nothing to install: this file belongs to arcasilesgroup/ai-engineering. 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/arcasilesgroup/ai-engineering/main/.agents/skills/ai-plan/SKILL.mdgit clone --depth 1 https://github.com/arcasilesgroup/ai-engineeringWrote 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/arcasilesgroup/ai-engineering/ai-plan)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-plan"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-plan.svg" alt="Measured on agentmods" 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 | $0.00121 | $0.00859 |
| Opus 5 | $0.00060 | $0.00430 |
| Sonnet 5 | $0.00024 | $0.00172 |
| Haiku 4.5 | $0.00012 | $0.00086 |
Grade A, and why
ai-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 7d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Turn the spec into tasks
What it produces
specs/NNN-slug/plan.md, beside the spec it implements.
Steps
- Read the spec, and only the specs it names — the one it supersedes, the ones it depends on. Nothing else. A hundred specs cost the index plus the ones you were told about.
- Write tasks small enough that each one is a commit, each one numbered and each one
opening with an empty box:
1. [ ] **Title** —. For every task, four things: file (the one it touches), check (the command that fails today and passes after), rollback (how to undo it), done when (in one sentence, testable). Never write[x]. The box is filled byai-eng spec show <id> --task <n> --tick, which runs the check and seals what it measured; an empty box means no command has run over these bytes yet, which is not the same as "not done". - A check is a command, never a judgement. "Looks right" is not a check. If a task's check reads "the agent decides X", say in one line why a script cannot do it — and if you cannot say why, write the script instead. That is rule 12, and it applies here first because here is where the cost is decided.
- If the spec adds anything that gets a URL, two tasks are mandatory and named:
- a CI/CD task: build, lint, test and security analysis on every push, deploy from the default branch, zero manual steps;
- an observability task covering the eight signals the spec lists, each passing with a command. Without them the plan is not finished, whatever else is in it.
- Order the tasks so that the first failing check appears as early as possible. A plan whose first six tasks cannot fail is a plan that finds out too late.
- Say what you are not doing, and why. The deliberate omissions are the part reviewers most often need and least often get.
Done when
- Every task has a file, a check, a rollback and a "done when".
- The deployable tasks exist if the spec is deployable, and are absent if it is not.
- The person has approved it, recorded as an ADR at the spec and plan digests. That record is the gate: no code before it.
- The reviewer got
ai-eng report view --spec <NNN>'sfile://link beside the Markdown; the ADR at the two digests stays the gate, and the page is how it is read.
What ships with it
1 file 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.
- 7d ago First seen · 62 lines · 121 tokens per session scan A 4f330b4dab58
ai-plan is a skill published in the GitHub repository arcasilesgroup/ai-engineering (54 stars, last pushed 5d ago), licensed Apache-2.0. It adds 121 tokens to every session and 859 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
My Skill
Content here.
task-generation
Reference material with the canonical task-format grammar and decomposition rules for plan-to-tasks expansion. Loaded on demand by generate-tasks; not directly invokable.
implementation-standards
Reference material with coding standards (defensive coding, error handling, testing patterns). Loaded on demand by the Developer sub-agent (.github/agents/developer.md); not directly invokable.
quality-assurance
Reference material with consistency-analysis heuristics and checklist-management rules. Loaded on demand by analyze-compliance and quality-control; not directly invokable.
writing-quality
Removes common AI writing patterns while preserving meaning, evidence, structure, and SDDP traceability. Ambient through AGENTS.md; not directly invokable.