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 learn-from-sagegit 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/learn-from-sage)<a href="https://agentmods.dev/skills/kirodotdev/kirocrew/learn-from-sage"><img src="https://agentmods.dev/badge/skills/kirodotdev/kirocrew/learn-from-sage/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/kirodotdev/kirocrew/learn-from-sage"><img src="https://agentmods.dev/badge/skills/kirodotdev/kirocrew/learn-from-sage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Rogue Agent · line 62 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00056 | $0.02271 |
| Opus 5.5 | $0.00022 | $0.00908 |
| Sonnet 5.5 | $0.00011 | $0.00454 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
learn-from-sage 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 3d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn from Sage — detection-gap (miss) analysis (V2, file-centric)
Learning is not summarizing what Sage found. It is finding issues that leaked past review and shipped, then working backwards to close the gap so the reviewer catches the next one.
Two files, one rule each:
learned-patterns.md— the canonical, consolidated ruleset. The only file a review loads as heuristics. Human-editable.learned-patterns.candidate.md— staging for new learnings. Staging an exact equivalent (same title, scope, impact and guidance) adds<!-- seen:N -->and<!-- last:<date> -->to the existing entry instead of appending; anything else is appended. Reviews do NOT read it. It is merged intolearned-patterns.mdonly when a human triggers consolidation (a one-shot AI merge), after which it's cleared.
The interpreter in every command below
Commands here are written as <python> …. Replace <python> with the absolute
interpreter path the task prompt names — it is the one the app itself runs, and
it is the only interpreter guaranteed to exist on this host. Never substitute a
bare python3: Windows has no such interpreter (the name resolves to a
Microsoft Store app-execution alias that runs nothing), so the command would
stage nothing and the learning would be silently lost. Outside a review
session, use the interpreter running the app.
Where every path below is rooted
Paths in this skill are relative to the app root — the directory holding
sage_lib/ and data/. A review or consolidation worker is already started
there, so sage_lib/store.py resolves without any prefix. Deliberately relative,
not ~/.kiro/crew/apps/code-review-sage/...: ~ is expanded by the SHELL, and
the task does not pin one — PowerShell expands it, cmd.exe passes it through
literally, and Python then cannot open the file. Outside a worker session, cd
into the app root first (~/.kiro/crew/apps/code-review-sage on macOS/Linux,
%USERPROFILE%\.kiro\crew\apps\code-review-sage on Windows).
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.
- 3d ago Changed · +3 lines effb17341400
- 13d ago Changed · +12 lines f1da7b9ca38e
- 1mo ago Changed · +14 lines 24a13ba279e3
- 1mo ago First seen · 137 lines · 56 tokens per session scan A 4f8f953ca149
learn-from-sage is a skill published in the GitHub repository kirodotdev/KiroCrew (4,518 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 2,271 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.
Other skills, from other repositories
review-fix-signoff-loop
Use when writing Agent Relay or Ricky workflows that must loop review, fix, and validation with fresh agent context until independent signoff agents, typically Claude and Codex, both agree the work is comprehensively complete. Covers fresh-context iterations, repairable gates, dual reviewer verdict contracts…
github-code-review
Review PRs: diffs, inline comments via gh or REST.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
simplify-code
Sequential 3-lens cleanup of recent code changes.
kodama-constraints
Enforce non-negotiable safety, scope, security, and quality constraints for implementation and review work.
review-animations
Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.