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 sfc-gh-myoung/ai_coding_rules --skill create-plangit clone --depth 1 https://github.com/sfc-gh-myoung/ai_coding_rulesWrote 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/sfc-gh-myoung/ai_coding_rules/create-plan)<a href="https://agentmods.dev/skills/sfc-gh-myoung/ai_coding_rules/create-plan"><img src="https://agentmods.dev/badge/skills/sfc-gh-myoung/ai_coding_rules/create-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 | $0.00135 | $0.02632 |
| Opus 5 | $0.00068 | $0.01316 |
| Sonnet 5 | $0.00027 | $0.00526 |
| Haiku 4.5 | $0.00014 | $0.00263 |
Grade A, and why
create-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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Generates architecturally rigorous, tactically concrete, safe-to-execute, and independently auditable implementation plans. Forces structural completeness so plan quality does not vary with the underlying model.
Use this skill when
- The user asks for a "plan", "design doc", "migration plan", or "refactor plan".
- Work spans multiple files, introduces architectural change, or has real tradeoffs between approaches.
- A pre-existing plan must be reviewed, upgraded, or standardized.
- Comparing multiple plans and producing a synthesized "best of" plan.
- The user wants a planning output they can hand to another engineer without follow-up questions.
Do not use for: single-line fixes, typos, obvious one-file changes, or pure "what/where is X?" research queries.
Inputs
Required
task: string — the specific implementation task the plan addresses. Must include enough context for the plan author to determine scope, current state, and target state.
Optional
codebase_root: path (default: current working directory) — directory to inventory before writing the plan.reference_plans: list of paths (default: none) — prior plans to synthesize from. When present, thePlan Provenancesection (15) becomes mandatory.runtime_targets: list (default: inferred) — explicit target runtime / versions that become hard constraints.out_of_scope: list (default: none) — items the user has already declared non-goals.
Outputs
Required
Write plan to: <plan_dir>/<slug>-<YYYY-MM-DD>.plan.md
plan_dirdefault:.snowflake/cortex/plans/if it exists, elsedocs/plans/, else./with a note.slug: lowercase-hyphens summary of the task (max 48 chars).- No overwrites: if file exists, append
-01.md,-02.md, etc. - End file with the literal line
**End of plan.**.
Workflow
Execute phases in order. Do not skip any.
Phase 1 — Research (mandatory before writing)
- Read the current state of the codebase relevant to
task. Inventory every file that will be created, modified, or deleted. Record paths. - Identify every caller / consumer of the thing being changed (grep for it). Note downstream breakage points.
- Identify the current runtime, package manager, test framework, CI system, and container base image. Do not assume — verify by reading config files.
- Surface orphan / missing files referenced by config (e.g.,
binentries pointing at nonexistent files). Flag them for Phase 0 of the plan. - If
reference_plansprovided, read each and note distinctive strengths / weaknesses per plan for Section 15. - Cite findings inline in the plan. Phase 1 discoveries (verified runtime versions, orphan config entries, downstream consumers, current base images) must appear explicitly in Sections 2 (Hard Constraints), 3 (Target Architecture), 9 (Risk Register), or 10 (Phase 0 tasks) — not just inform the plan silently. A reader must be able to trace every non-trivial claim back to a file path or command output.
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 · 181 lines · 135 tokens per session scan A 2fa72a840a37
create-plan is a skill published in the GitHub repository sfc-gh-myoung/ai_coding_rules (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 135 tokens to every session and 2,632 once invoked, about $0.0007 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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