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 Kyne0328/rel-ai-local-coding-agent --skill rel-ai-planninggit clone --depth 1 https://github.com/Kyne0328/rel-ai-local-coding-agentWrote 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/kyne0328/rel-ai-local-coding-agent/rel-ai-planning)<a href="https://agentmods.dev/skills/kyne0328/rel-ai-local-coding-agent/rel-ai-planning"><img src="https://agentmods.dev/badge/skills/kyne0328/rel-ai-local-coding-agent/rel-ai-planning/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/kyne0328/rel-ai-local-coding-agent/rel-ai-planning"><img src="https://agentmods.dev/badge/skills/kyne0328/rel-ai-local-coding-agent/rel-ai-planning.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 | $0.00053 | $0.00429 |
| Opus 5 | $0.00026 | $0.00215 |
| Sonnet 5 | $0.00011 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
rel-ai-planning 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 11d 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 — 26 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rel.AI Planning
Reuse the active work_id opened by rel-ai-workflow. Do not call relai_work with action: "begin" when the same objective already has a work session.
Use this skill only when architecture or sequencing is genuinely non-trivial. Do not trigger for small localized changes whose implementation path is already clear after targeted inspection.
Planning defines architecture, dependencies, completion conditions, and meaningful risks; it does not prescribe a fixed tool ritual. During execution, runtime workflow guidance (workflow.recommendedActions and workflow.avoidActions) calibrates the exact next repository action for the current evidence and boundary.
Planning workflow
- Use the existing bootstrap and inspect the current implementation before designing a replacement.
- Choose the shortest coherent architecture that satisfies the current requirement. Reuse appropriate existing boundaries; do not add abstractions for hypothetical future needs.
- Define ordered tasks with explicit completion conditions, dependencies, and the validation needed to prove each meaningful risk.
- Keep small plans compact. Use a durable checkbox plan only when the work benefits from persistent multi-step tracking.
- For a durable plan, update checkboxes as completion conditions are met. After Task N, perform cumulative consolidation across Tasks 1..N: remove duplication, collapse unnecessary layers, and keep the combined implementation simpler than the sum of its steps.
- Separate repository-verifiable work from genuinely external or manual-only steps so execution does not stop for work the agent can complete itself.
- Hand execution back to
rel-ai-workflowwith the chosen architecture, task order, completion conditions, dependencies, validation expectations, and any unresolved decisions.
Replanning boundary
Do not reopen planning for ordinary implementation details. Replan only when new evidence invalidates the architecture, task ordering, dependencies, or completion conditions.
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.
- 11d ago First seen · 26 lines · 53 tokens per session scan A a308898633b2
rel-ai-planning is a skill published in the GitHub repository Kyne0328/rel-ai-local-coding-agent (19 stars, last pushed 6d ago), licensed Apache-2.0. It adds 53 tokens to every session and 429 once invoked, about $0.0003 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.
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