Borrowing it
Nothing to install: this file belongs to fokkerone/superspecs. 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/fokkerone/superspecs/main/.skills/plan-discuss/SKILL.mdgit clone --depth 1 https://github.com/fokkerone/superspecsWrote 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/fokkerone/superspecs/plan-discuss)<a href="https://agentmods.dev/skills/fokkerone/superspecs/plan-discuss"><img src="https://agentmods.dev/badge/skills/fokkerone/superspecs/plan-discuss/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/fokkerone/superspecs/plan-discuss"><img src="https://agentmods.dev/badge/skills/fokkerone/superspecs/plan-discuss.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 | $0.00048 | $0.01001 |
| Opus 5 | $0.00024 | $0.00500 |
| Sonnet 5 | $0.00010 | $0.00200 |
| Haiku 4.5 | $0.00005 | $0.00100 |
Grade A, and why
plan-discuss 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 8d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: plan-discuss
You are opening the discussion phase. This happens before any spec is written.
The goal is to capture every decision, assumption, constraint, and open question so the spec that follows is grounded — not guessed.
Why this step exists
Decisions made during planning are cheap. Decisions discovered during implementation are expensive. This step makes the cheap ones explicit before they become expensive ones.
Steps
1. Orient with the wiki
Before asking anything, scan the wiki for relevant context using tiered retrieval:
Tier 1 — Index scan (always):
- Read
superspec/wiki/Home.md— domain catalog and recent ingest activity - Read only the frontmatter (
title,tags,summary) of every page insuperspec/wiki/(skipraw/,.obsidian/) - Score each page for relevance to the feature being described
Tier 2 — Deep read (targeted):
- Open the full body of the 3–5 most relevant pages
- Follow
[[wikilinks]]one level deep for directly related pages
Report before the first question:
- List relevant pages found with a one-line summary of what each contributes
- Flag decisions, patterns, or interfaces that should inform or constrain this discussion
- If the wiki is empty or nothing is relevant: say so explicitly — "Wiki has no relevant pages for this feature."
2. Understand the rough idea
Ask the user to describe what they want to build. Keep it conversational. One question at a time. Cover:
What
- What does this feature do from the user's perspective?
- What problem does it solve?
Why now
- What's the trigger for building this?
- What happens if we don't build it?
Constraints
- Technical constraints (existing stack, performance requirements, etc.)
- Scope constraints (what's explicitly NOT included?)
- Time or complexity constraints?
Success
- How do we know this is done?
- What does "working" look like?
Risks
- What could go wrong?
- What are we uncertain about?
Do not ask all questions at once. Read the answers and ask follow-ups. Stop when you have enough to write the discussion doc.
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.
- 8d ago First seen · 157 lines · 48 tokens per session scan A d19bb1c4bfa4
plan-discuss is a skill published in the GitHub repository fokkerone/superspecs (4 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 1,001 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…