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/commands/oriolshhh/runware-image-mcp/council)<a href="https://agentmods.dev/commands/oriolshhh/runware-image-mcp/council"><img src="https://agentmods.dev/badge/commands/oriolshhh/runware-image-mcp/council/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/commands/oriolshhh/runware-image-mcp/council"><img src="https://agentmods.dev/badge/commands/oriolshhh/runware-image-mcp/council.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 | $0.00058 | $0.01523 |
| Opus 5 | $0.00029 | $0.00762 |
| Sonnet 5 | $0.00012 | $0.00305 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
council 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/council — Convene the solution council
Purpose
Produce a defensible implementation recommendation for an ambiguous or high-impact
request, using only the specialists the decision needs. The council never writes
production code; it stops at human approval and hands off to /spec.
Invocation
/council <feature idea, problem, or proposed change>
Examples
/council add multi-tenant API keys with per-tenant rate limits/council should we replace our bespoke cache with Redis?/council the importer is slow on large files — what should we change?/council migrate the config format from JSON to YAML
Accepted input
A feature idea, problem statement, or proposed change — however rough.
Prerequisites
- A HarnessKit workspace (
harnesskit init). - Runs inside the consuming AI coding tool; the HarnessKit CLI calls no model API.
- Fresh
.agent/context/is recommended; run/build-contextfirst if missing or stale.
Procedure
- Discovery —
product-discoveryreuses.agent/context/viacontext-discovery, inspects repository evidence, and produces a decision brief (facts, assumptions, preferences, blockers). Ask at most 3 (max 5) grouped, decision-changing questions; otherwise state defaults and continue. Build one task context capsule and pass it with the brief to every specialist so they do not independently reload the context pack. - Routing —
council-routerselects the smallest relevant specialist set (always architecture and product; add security, testing, operations, UX, data, frontend design/system/accessibility/motion, or others only on evidence) and explains each choice. Skip the full council for trivial, low-risk changes. Applymodel-routing: the router may be light, each specialist uses its declared default or an evidence-based escalation, and chair synthesis stays heavy/high. Never use a light model to adjudicate heavy specialist reports. - Independent review — each selected specialist produces a first-pass report from the brief without seeing the others' reports. Run them in the strongest isolation your platform supports (see Execution modes).
- Challenge — exactly one bounded round.
devils-advocate(plus selected specialists) challenges the leading proposal with evidence-based objections, unsupported assumptions, and premature-consensus checks. No further rounds. - Synthesis —
council-chairintegrates reports and rebuttals into one decision record (recommended path, confidence, rejected alternatives, risks, preserved dissent, unresolved questions). It synthesizes; it does not vote. - Approval — stop and present the decision record for explicit human approval.
- Handoff — only after approval, run
/specto enter spec-to-implementation. The council itself writes no production code.
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 · 146 lines · 58 tokens per session scan A 21369217c566
council is a command published in the GitHub repository Oriolshhh/runware-image-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 1,523 once invoked, about $0.0003 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.