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 wiolett-industries/marketplace --skill context-discoverygit clone --depth 1 https://github.com/wiolett-industries/marketplaceWrote 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/wiolett-industries/marketplace/context-discovery)<a href="https://agentmods.dev/skills/wiolett-industries/marketplace/context-discovery"><img src="https://agentmods.dev/badge/skills/wiolett-industries/marketplace/context-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/wiolett-industries/marketplace/context-discovery"><img src="https://agentmods.dev/badge/skills/wiolett-industries/marketplace/context-discovery.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 | $0.00696 |
| Opus 5.5 | $0.00019 | $0.00278 |
| Sonnet 5.5 | $0.00010 | $0.00139 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
context-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 2mo 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Discovery
Gather only enough context for a confident execution decision or decision-complete durable plan. Ask only questions whose answers can change scope, architecture, risk, acceptance criteria, or user-visible behavior.
Question Budget
- Start at the named working surface; inspect only the smallest code, test, or manifest evidence that resolves the question.
- Ask one grouped question batch for the remaining material branches.
- Use a second batch only if the first answer reveals a new costly or irreversible branch.
- For reversible fast/standard decisions, proceed with an explicit reasonable assumption when clarification would cost more than correction.
- Stop when another answer would not change the implementation, primary path, or assurance profile.
Do not ask for discoverable facts, preferences that do not affect the result, or exhaustive edge cases outside the requested scope.
UI Discovery
For production UI work, including small changes when the request names an existing/shared component, complete the ui-contract reuse gate before proposing components or visual rules. Inspect the closest screen, primitives, and visual source of truth; record receipt paths/exports, reuse/adapt/none, and structural verification in chat, plan, or handoff. Named reuse is architecture acceptance, not visual guidance.
Read Budget
These are hard gates. Count new file output; prefer slices over whole files.
fast: diff or named surface; at most three files, two searches, and 12 KB.standard: at most four files, three searches, and 16 KB initially; eight files or 25 KB total unless the next read answers a named dependency.assuranceor explicit audit: declare the extra surface before widening and keep each pass tied to a named risk.
No whole-repository scan without a named unknown. Stop when the direct path and tests are known. A new plan, compaction, retry, hand-off, or correction never resets these totals. At the cap, proceed, shrink, ask, or name the gap.
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.
- 2mo ago First seen · 45 lines · 48 tokens per session scan A 0c9db486df8a
context-discovery is a skill published in the GitHub repository wiolett-industries/marketplace (10 stars, last pushed 15d ago), licensed MIT. It adds 48 tokens to every session and 696 once invoked, about $0.0002 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 2026-08-30.
context-discovery compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| context-discoverywiolett-industries/marketplace | Skill | 10 | 15d ago | MIT | A | 48 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |