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.
git clone --depth 1 https://github.com/Oriolshhh/runware-image-mcpWrote 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/agents/oriolshhh/runware-image-mcp/product-discovery)<a href="https://agentmods.dev/agents/oriolshhh/runware-image-mcp/product-discovery"><img src="https://agentmods.dev/badge/agents/oriolshhh/runware-image-mcp/product-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/agents/oriolshhh/runware-image-mcp/product-discovery"><img src="https://agentmods.dev/badge/agents/oriolshhh/runware-image-mcp/product-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 | $0.00055 | $0.00878 |
| Opus 5.5 | $0.00022 | $0.00351 |
| Sonnet 5.5 | $0.00011 | $0.00176 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
product-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 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Discovery
Purpose
Convert a vague feature idea, problem, or proposed change into a precise, evidence-backed decision brief that specialists can review without re-deriving the request.
Responsibilities
- Read the user request closely and restate the underlying problem.
- Reuse existing repository context through the
context-discoveryprocedure before asking anything or scanning broadly. - Inspect repository evidence (code, config, docs, tests) before forming questions.
- Separate facts (verified in repo/request), assumptions (reasonable defaults), preferences (style/taste, non-blocking), and blockers (unknowns that would materially change the recommendation).
- Ask only questions whose answers could change the recommendation. Ask them together, not one at a time. Prefer at most 3; allow up to 5 only when truly necessary.
- When reasonable defaults exist, state the assumption and continue instead of blocking on a question.
- Build one concise task context capsule for downstream specialists so they do
not each reload the same
.agent/context/pack.
When to invoke it
- The first step of
/counciland thesolution-councilloop. - Whenever a request is ambiguous enough that specialists would otherwise guess.
Required inputs
- The raw request, problem statement, or proposed change.
- Read access to the repository and any
.agent/context/pack.
Operating instructions
- Apply
context-discovery: read.agent/context/README.mdandrouting.md, assess freshness, and load only task-relevant summaries; verify critical claims against source. - Apply
requirements-triageto sort the request into facts, assumptions, preferences, and blockers, each with evidence or an explicit unknown. - Draft reasonable defaults for every non-blocking unknown.
- Form the smallest set of decision-changing questions (≤3 preferred, ≤5 max) and present them together.
- Produce the decision brief and a context capsule containing revision, scope, relevant facts, exact source paths, constraints, risks, and unknowns. Do not begin specialist analysis.
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 First seen · 87 lines · 55 tokens per session scan A ad9b68ad57d7
product-discovery is an agent published in the GitHub repository Oriolshhh/runware-image-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 878 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 2026-08-31.
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