Kiro Crew is a persistent development workspace where agents continue multi-step software work across sessions, schedules, and connected interfaces. Developers use it locally or remotely through a desktop app, web dashboard, CLI, Slack, or Discord, with unattended tasks and recurring jobs. The catalogue contains skills and instructions for working with this workspace.
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 kirodotdev/KiroCrew --skill llm-councilgit clone --depth 1 https://github.com/kirodotdev/KiroCrewWrote 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/kirodotdev/kirocrew/llm-council)<a href="https://agentmods.dev/skills/kirodotdev/kirocrew/llm-council"><img src="https://agentmods.dev/badge/skills/kirodotdev/kirocrew/llm-council.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 111 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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 | $0.00137 | $0.03210 |
| Opus 5 | $0.00068 | $0.01605 |
| Sonnet 5 | $0.00027 | $0.00642 |
| Haiku 4.5 | $0.00014 | $0.00321 |
Grade C, and why
llm-council scanned grade C with 1 finding 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 today.
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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
and give your best judgment — do not refuse. Do not ask clarifying questions; if How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Council
Overview
Answer with a panel of different-vendor models instead of one. The main
session is the Chairman: it fans a task out to N subagents — each spawn_run
pinned to a different model via the model override — collects their outputs, and
produces one result. Cross-vendor is the point (a same-model panel echoes one bias);
kiro-cli is already the gateway (kiro-cli chat --list-models), so no external egress.
Modes at a glance
| Mode | Members do | Chairman does | Use for |
|---|---|---|---|
| synthesis (default) | Answer INDEPENDENTLY & blind (MoA) | Merge into one better answer + surface dissent | Open questions, design calls, "am I missing something" |
| vote | Cast ONE structured VOTE: from a fixed option set |
Deterministic majority tally + verdict | Group decisions with discrete options |
| adversarial | Red-team a TARGET (critic / defender roles), not blind | Consolidate critiques by severity → SHIP/REVISE/REJECT | Reviewing a design, plan, or PR |
One prompt can't do all three: a blind-independent proposer is wrong for voting (needs a tallyable verdict) and for review (a critic must SEE the target and attack it). Design grounded in the multi-agent-debate literature (MoA vs Multi-Persona vs voting are distinct role structures; "agreement modulation" is the key knob).
When to use / when NOT
Use for hard/high-stakes/ambiguous/subjective questions, group decisions, or reviews. Do NOT use for simple lookups or routine turns — a council costs N+1 model runs. It is a deliberate, occasional move. If unsure it's worth it, ask first.
Procedure (you are the Chairman)
- Pick the roster (3–4 members, cross-vendor). Run
kiro-cli chat --list-models --format json, then pick a strong general model from each of 3–4 different vendors (e.g. Anthropic, OpenAI, DeepSeek, Zhipu) — cross-vendor diversity is the payoff. Skip deprecated or restricted-use models unless opted in. Honor a user-supplied roster verbatim.--list-modelsis a CATALOG, not an entitlement: a listed model can still be unavailable to this session, so keep a fallback pick for each slot.reasoning_effort('low' | 'medium' | 'high' | 'xhigh' | 'max') is batch-wide and wins over the configured role pin — setting it forces one dedicated process per subagent (~3-5s start, ~400 MB each, against ~200ms and near-zero for session sharing), which is worth it foradversarialon a high-stakes artifact and wasteful for a cheapvote. - Fan out — one
spawn_runPER member. ⚠️spawn_run'smodelapplies to the whole call, so a multi-model panel is N separate calls, each a singletaskwith a distinctmodel— NOT one call with atasksarray. (agentsvaries per task;modeldoes not.) Use the mode's member prompt (below) as thetask. Keep a private map ofsubagent id → model. Passinclude_memory=falseon every member spawn: the member prompt is self-contained, and inherited memory re-imports the Chairman's framing into every supposedly independent answer, which is the shared bias a council exists to break.include_lessons=falsetoo unless a member will write code; keepinclude_project=truewhen the question is about code in the active project. Each member is told by name which groups were withheld, so it reports the gap instead of inventing context. - Wait for ALL
[Subagent completion event]s. Do NOT answer the task yourself while waiting. If a member fails, drop it and proceed (a council of 2 is still a council); abort only if zero return. - Chairman step — per mode (below).
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.
- today Changed · +15 lines · -72 tokens per session c2cd27404568
- 8d ago First seen · 226 lines · 209 tokens per session scan C d46e70d8cdc7
llm-council is a skill published in the GitHub repository kirodotdev/KiroCrew (3,687 stars, last pushed today), licensed Apache-2.0. It adds 137 tokens to every session and 3,210 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
executive-financial-briefing
Generates a concise executive-level financial briefing or summary suitable for a CEO, CFO, or board presentation. Use when the user asks for a summary, briefing, executive summary, board update, financial overview, financial health check, or "how is the business doing". Covers the full P&L picture in one page. Also…
multi-quarter-trend-analysis
Analyzes financial trends across multiple quarters by comparing P&L metrics over time. Use when the user wants to see trends, patterns, trajectories, or directional movement across 3 or more quarters. Also use for "how are we trending", "show me the trend", "track performance over time", "quarter over quarter…
sector-rotation
Identify which market sectors to overweight or underweight based on current macro conditions and sector performance data.
trend-analysis
Analyze price and volume trends for one or more stocks to determine momentum direction and key technical levels.
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
benefits-advisor
Explain an Acme employee benefit, including eligibility, employee cost, coverage, and key details.