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 rube-de/cc-skills --skill feature-discoverygit clone --depth 1 https://github.com/rube-de/cc-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/rube-de/cc-skills/feature-discovery)<a href="https://agentmods.dev/skills/rube-de/cc-skills/feature-discovery"><img src="https://agentmods.dev/badge/skills/rube-de/cc-skills/feature-discovery/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/rube-de/cc-skills/feature-discovery"><img src="https://agentmods.dev/badge/skills/rube-de/cc-skills/feature-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00159 | $0.01785 |
| Opus 5 | $0.00079 | $0.00892 |
| Sonnet 5 | $0.00032 | $0.00357 |
| Haiku 4.5 | $0.00016 | $0.00178 |
Grade A, and why
feature-discovery 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 10d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature discovery
Turns "what should we build next?" into a grounded, ranked roadmap by fanning out many agents instead of one agent guessing. The whole run is a deterministic Workflow script, so every invocation follows the same pipeline rather than being re-improvised.
Distinct from brainstorm. pm:brainstorm goes deep on one known-ish feature
(interactive Q&A, a single spec doc). This goes wide across the whole product and
returns a ranked roadmap of many candidate features. Reach for brainstorm to design
how to build a chosen feature; reach for this to discover what to build.
When this fits
Use it when the user wants ideas that are researched, deduped, specced, and pressure-tested, not a quick off-the-cuff list. It is deliberately heavyweight (roughly 35-40 agents at exhaustive depth), so for a casual "give me three ideas" just answer directly.
The pipeline (what the script runs)
The Workflow engine runs five phases - you do not run these yourself, the engine does (see scripts/feature-discovery.workflow.js):
- Ground - one agent maps the current product from the repo (entry points,
routes, data models, content, services, config, docs); in parallel a planner
proposes competitor segments and one analyst researches each via web search. The
product is always mapped, even in
internalscope, so ideation never re-proposes what exists. - Ideate - one agent per value lens (discoverability, core value, UX, monetization, engagement, trust, information architecture), each grounded in the inventory and competitor findings, each forbidden from proposing anything that already exists.
- Shortlist - a single curator merges duplicates, drops the trivial and the
already-built, ranks by value-to-effort, and picks the top 8-12 (5-6 at
depth: quick). - Spec & Validate - a pipeline per feature: spec it, then an adversarial skeptic scores novelty, real value, feasibility in the current architecture, competitor precedent, and maintenance burden, returning build / maybe / drop with a confidence score.
- Synthesize - one agent writes the final Markdown report: exec summary, gaps (internal and versus competitors), a ranked roadmap table, full specs for the build/maybe features, dropped ideas with reasons, and a quick-wins-vs-bigger-bets split.
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.
- 10d ago First seen · 133 lines · 159 tokens per session scan A 8973f9d506b1
feature-discovery is a skill published in the GitHub repository rube-de/cc-skills (10 stars, last pushed 4d ago), licensed MIT. It adds 159 tokens to every session and 1,785 once invoked, about $0.0008 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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