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 n-n-code/n-n-code-skills --skill story-to-plan-orchestratorgit clone --depth 1 https://github.com/n-n-code/n-n-code-skillsWrote 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/n-n-code/n-n-code-skills/story-to-plan-orchestrator)<a href="https://agentmods.dev/skills/n-n-code/n-n-code-skills/story-to-plan-orchestrator"><img src="https://agentmods.dev/badge/skills/n-n-code/n-n-code-skills/story-to-plan-orchestrator/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/n-n-code/n-n-code-skills/story-to-plan-orchestrator"><img src="https://agentmods.dev/badge/skills/n-n-code/n-n-code-skills/story-to-plan-orchestrator.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 | $0.00083 | $0.02702 |
| Opus 5.5 | $0.00033 | $0.01081 |
| Sonnet 5.5 | $0.00017 | $0.00540 |
| Haiku 4.5 | $0.00008 | $0.00270 |
Grade A, and why
story-to-plan-orchestrator 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 1mo 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Story-To-Plan Orchestrator
Coordinate the path from product intent to an evidence-backed implementation plan. Let each component skill own its artifact; this skill owns routing, invalidation, resumption, and packet assembly.
This is a process overlay and multi-stage entry point. The component skills remain independently usable.
For a review-only request, assess packet coherence and readiness and return findings without rebuilding artifacts. Resume or repair stages when requested. Return artifacts in the conversation unless file output was requested; pipeline preparation does not authorize product implementation or external publication.
When To Use
- produce a complete Story Card, Repo Context, and Implementation Plan handoff
- run or repair two or more preparation stages
- select and materialize one slice from a Split Story Set before scouting
- resume or validate a partial packet whose next valid stage is unclear
- reconcile stale or contradictory stage outputs before coding
Not For
- one requested artifact; use its owning component skill
- direct code implementation after preparation
- broad product strategy, architecture review, test strategy, or security review
Ownership
| Owner | Artifact | Decisions it owns |
|---|---|---|
story-clarifier |
Story Card or Split Story Set | behavior, scope, acceptance, and story readiness |
story-repo-scout |
Repo Context | repository evidence, proposed-path basis, and repo-context readiness |
story-implementation-planner |
Implementation Plan | executor fit, task shape, validation, and plan readiness |
| this skill | Preparation Packet | packet status, stage routing, invalidation, slice selection, resumption, and coherence |
Treat each component's artifact schema and stage-specific readiness evidence as authoritative. The shared three-field protocol below is the family-level rule. Do not copy a component's full schema, anti-pattern list, or executor rules into this skill.
Shared Stage Protocol
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.
- 1mo ago Changed · +5 lines 607dcf680322
- 1mo ago First seen · 269 lines · 83 tokens per session scan A 03ab6121aadf
story-to-plan-orchestrator is a skill published in the GitHub repository n-n-code/n-n-code-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 2,702 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
astro-dso-doc
Bootstraps a deep-sky object (DSO) astrophotography project from raw acquisition frames (own rig, not a smart telescope), AND generates a complete, polished HTML documentation page, a processing checklist, an AstroBin post JSON, a PixInsight process icon set (XPSM), and a ready-to-paste PixInsight project Description…
logo-design
Professional logo and brand-mark design, from brief to production files. Guides discovery and the design brief, concept generation, choosing a mark type (wordmark, monogram, letterform, pictorial, abstract, emblem, mascot, combination), building clean geometric SVG logos, optical refinement, colour and typography…
llm-council
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this'…
document-code
Apply Google Style documentation standards to Python, Go, TypeScript, and Terraform code. Use when writing or reviewing code that needs docstrings/comments/JSDoc, when asked to "document this code", "add docstrings", "follow Google Style", or when improving code documentation quality. Supports Python docstrings, Go…
work-on-ticket
Fetches Jira ticket details, creates an appropriately named branch, and initiates the task planning workflow. Use when the user says "work on [TICKETID]" or similar phrases.
datadog
Use this skill when you need to search Datadog logs, query metrics, tail logs in real-time, trace distributed requests, investigate errors, compare time periods, find log patterns, check service health, or export observability data.