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 agentmods add skills/kirodotdev/kirocrew/ai-discovernpx skills add kirodotdev/KiroCrew --skill ai-discovergit 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/ai-discover)<a href="https://agentmods.dev/skills/kirodotdev/kirocrew/ai-discover"><img src="https://agentmods.dev/badge/skills/kirodotdev/kirocrew/ai-discover.svg" alt="Measured on agentmods" 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 | $0.00075 | $0.00639 |
| Opus 5 | $0.00037 | $0.00319 |
| Sonnet 5 | $0.00015 | $0.00128 |
| Haiku 4.5 | $0.00007 | $0.00064 |
Grade A, and why
ai-discover 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 6d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-discover — the discovery step of the auto-improvement loop
This skill drives Phase A (discovery) of an auto-improvement cycle. It is discovery only: it never applies a change, never runs the keep-or-revert A/B, and never decides whether a finding is kept or drafted as a pull request. Those are the spine's deterministic Python gate / keeper / pipeline, which no model can argue past. That separation is the point — the measurement is the product.
Perf track
- Fan out one subagent per candidate hot-path area.
- Each runs the profiler against a realistic workload and finds the single largest behavior-preserving win in its area.
- Each returns exactly ONE candidate:
{target locus, signature, hypothesis, expected stage win, scenario}. - Write all candidates to the discovery artifact the spine reads. The spine dedups by content fingerprint, implements, and verifies with a serial pinned A/B measurement. Discovery does none of that.
Bug track (kind="bug")
- Fan out one subagent per failure surface — a risky or known-fragile path.
- Each writes a minimal deterministic reproducing test that FAILS on the base commit (RED) and a fix that makes it PASS (GREEN) without regressing the rest of the suite.
- Each returns
{target locus, reproducing test id + path, fix diff, blast radius, severity note}. The spine's RED → GREEN → STAYGREEN gate is the verdict; there is no A/B or noise band for bug findings.
Reporting a candidate honestly
State the expected win as a hypothesis, not a result. The spine measures it; if your estimate was wrong the candidate is reverted and that is a normal, useful outcome. A guess presented as a measurement is the failure mode this whole app exists to prevent.
What this skill never does
- Never edits the ruler, the measurement harness, the tests-of-record, or anything outside the active target profile's edit allowlist. Those paths are mechanically rejected, so an edit there wastes the whole cycle.
- Never publishes or merges a pull request. Survivors are drafted as GitHub draft PRs by the spine, and a human publishes them.
- Never fabricates a measured number. A fabricated win is the worst possible reward-hack, because it corrupts the record the loop reasons from.
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.
- 6d ago First seen · 53 lines · 75 tokens per session scan A 4eb65ed33cb0
ai-discover is a skill published in the GitHub repository kirodotdev/KiroCrew (3,646 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 639 once invoked, about $0.0004 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 2026-08-30.
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