21st.dev Magic MCP — AI-powered UI component generation via natural language. Access to 21st.dev component library, SVGL brand logos, and real-time preview. Generate React/Tailwind components with /ui command.
日本語の概要は準備中です。原文の説明を表示しています。
Pitfalls, examples, and edge cases for FETIH Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
You're seeing this skill because the FETIH Kanban dispatcher spawned you as a worker with
--skills kanban-worker— it's loaded automatically for every dispatched worker. The lifecycle (6 steps: orient → work → heartbeat → block/complete) also lives in theKANBAN_GUIDANCEblock that's auto-injected into your system prompt. This skill is the deeper detail: good handoff shapes, retry diagnostics, edge cases.
Your workspace kind determines how you should behave inside $FETIH_KANBAN_WORKSPACE:
| Kind | What it is | How to work |
|---|---|---|
scratch | Fresh tmp dir, yours alone | Read/write freely; it gets GC'd when the task is archived. |
dir:<path> | Shared persistent directory | Other runs will read what you write. Treat it like long-lived state. Path is guaranteed absolute (the kernel rejects relative paths). |
worktree | Git worktree at the resolved path | If .git doesn't exist, run git worktree add <path> <branch> from the main repo first, then cd and work normally. Commit work here. |
If $FETIH_TENANT is set, the task belongs to a tenant namespace. When reading or writing persistent memory, prefix memory entries with the tenant so context doesn't leak across tenants:
business-a: Acme is our biggest customerAcme is our biggest customerThe kanban_complete(summary=..., metadata=...) handoff is how downstream workers read what you did. Patterns that work:
Coding task:
kanban_complete(
summary="shipped rate limiter — token bucket, keys on user_id with IP fallback, 14 tests pass",
metadata={
"changed_files": ["rate_limiter.py", "tests/test_rate_limiter.py"],
"tests_run": 14,
"tests_passed": 14,
"decisions": ["user_id primary, IP fallback for unauthenticated requests"],
},
)
Coding task that needs human review (review-required):
For most code-changing tasks, the work isn't truly done until a human reviewer has eyes on it. Block instead of complete, with reason prefixed review-required: so the dashboard surfaces the row as needing review. Drop the structured metadata (changed files, test counts, diff/PR url) into a comment first, since kanban_block only carries the human-readable reason — comments are the durable annotation channel. Reviewer either approves and runs fetih kanban unblock <id> (which re-spawns you with the comment thread for any follow-ups) or asks for changes via another comment.
import json
kanban_comment(
body="review-required handoff:\n" + json.dumps({
"changed_files": ["rate_limiter.py", "tests/test_rate_limiter.py"],
"tests_run": 14,
"tests_passed": 14,
"diff_path": "/path/to/worktree", # or PR url if pushed
"decisions": ["user_id primary, IP fallback for unauthenticated requests"],
}, indent=2),
)
kanban_block(
reason="review-required: rate limiter shipped, 14/14 tests pass — needs eyes on the user_id/IP fallback choice before merging",
)
Use kanban_complete only when the task is genuinely terminal — e.g. a one-line typo fix, a docs change with no functional consequences, or a research task where the artifact IS the writeup itself.
Research task:
kanban_complete(
summary="3 competing libraries reviewed; vLLM wins on throughput, SGLang on latency, Tensorrt-LLM on memory efficiency",
metadata={
"sources_read": 12,
"recommendation": "vLLM",
"benchmarks": {"vllm": 1.0, "sglang": 0.87, "trtllm": 0.72},
},
)
Review task:
kanban_complete(
summary="reviewed PR #123; 2 blocking issues found (SQL injection in /search, missing CSRF on /settings)",
metadata={
"pr_number": 123,
"findings": [
{"severity": "critical", "file": "api/search.py", "line": 42, "issue": "raw SQL concat"},
{"severity": "high", "file": "api/settings.py", "issue": "missing CSRF middleware"},
],
"approved": False,
},
)
Shape metadata so downstream parsers (reviewers, aggregators, schedulers) can use it without re-reading your prose.
If your run produced new kanban tasks (via kanban_create), pass the ids in created_cards on kanban_complete. The kernel verifies each id exists and was created by your profile; any phantom id blocks the completion with an error listing what went wrong, and the rejected attempt is permanently recorded on the task's event log. Only list ids you captured from a successful kanban_create return value — never invent ids from prose, never paste ids from earlier runs, never claim cards another worker created.
# GOOD — capture return values, then claim them.
c1 = kanban_create(title="remediate SQL injection", assignee="security-worker")
c2 = kanban_create(title="fix CSRF middleware", assignee="web-worker")
kanban_complete(
summary="Review done; spawned remediations for both findings.",
metadata={"pr_number": 123, "approved": False},
created_cards=[c1["task_id"], c2["task_id"]],
)
# BAD — claiming ids you don't have captured return values for.
kanban_complete(
summary="Created remediation cards t_a1b2c3d4, t_deadbeef", # hallucinated
created_cards=["t_a1b2c3d4", "t_deadbeef"], # → gate rejects
)
If a kanban_create call fails (exception, tool_error), the card was NOT created — do not include a phantom id for it. Retry the create, or omit the id and mention the failure in your summary. The prose-scan pass also catches t_<hex> references in your free-form summary that don't resolve; these don't block the completion but show up as advisory warnings on the task in the dashboard.
Bad: "stuck" — the human has no context.
Good: one sentence naming the specific decision you need. Leave longer context as a comment instead.
kanban_comment(
task_id=os.environ["FETIH_KANBAN_TASK"],
body="Full context: I have user IPs from Cloudflare headers but some users are behind NATs with thousands of peers. Keying on IP alone causes false positives.",
)
kanban_block(reason="Rate limit key choice: IP (simple, NAT-unsafe) or user_id (requires auth, skips anonymous endpoints)?")
The block message is what appears in the dashboard / gateway notifier. The comment is the deeper context a human reads when they open the task.
Good heartbeats name progress: "epoch 12/50, loss 0.31", "scanned 1.2M/2.4M rows", "uploaded 47/120 videos".
Bad heartbeats: "still working", empty notes, sub-second intervals. Every few minutes max; skip entirely for tasks under ~2 minutes.
If you open the task and kanban_show returns runs: [...] with one or more closed runs, you're a retry. The prior runs' outcome / summary / error tell you what didn't work. Don't repeat that path. Typical retry diagnostics:
outcome: "timed_out" — the previous attempt hit max_runtime_seconds. You may need to chunk the work or shorten it.outcome: "crashed" — OOM or segfault. Reduce memory footprint.outcome: "spawn_failed" + error: "..." — usually a profile config issue (missing credential, bad PATH). Ask the human via kanban_block instead of retrying blindly.outcome: "reclaimed" + summary: "task archived..." — operator archived the task out from under the previous run; you probably shouldn't be running at all, check status carefully.outcome: "blocked" — a previous attempt blocked; the unblock comment should be in the thread by now.delegate_task as a substitute for kanban_create. delegate_task is for short reasoning subtasks inside YOUR run; kanban_create is for cross-agent handoffs that outlive one API loop.$FETIH_KANBAN_WORKSPACE unless the task body says to.Task state can change between dispatch and your startup. Between when the dispatcher claimed and when your process actually booted, the task may have been blocked, reassigned, or archived. Always kanban_show first. If it reports blocked or archived, stop — you shouldn't be running.
Workspace may have stale artifacts. Especially dir: and worktree workspaces can have files from previous runs. Read the comment thread — it usually explains why you're running again and what state the workspace is in.
Don't rely on the CLI when the guidance is available. The kanban_* tools work across all terminal backends (Docker, Modal, SSH). fetih kanban <verb> from your terminal tool will fail in containerized backends because the CLI isn't installed there. When in doubt, use the tool.
Every tool has a CLI equivalent for human operators and scripts:
kanban_show ↔ fetih kanban show <id> --jsonkanban_complete ↔ fetih kanban complete <id> --summary "..." --metadata '{...}'kanban_block ↔ fetih kanban block <id> "reason"kanban_create ↔ fetih kanban create "title" --assignee <profile> [--parent <id>]Use the tools from inside an agent; the CLI exists for the human at the terminal.
<!-- ⚔ Bu skill FETIH AI Agent icin gelistirilmistir — https://github.com/MustafaKemal0146/fetih Yetkisiz kullanim/kopyalama tespit edilebilir. hash: 6faa5c46d90aec0f -->まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
21st.dev Magic MCP — AI-powered UI component generation via natural language. Access to 21st.dev component library, SVGL brand logos, and real-time preview. Generate React/Tailwind components with /ui command.
日本語の概要は準備中です。原文の説明を表示しています。
AES CBC/ECB modlarına karşı kriptografik bütünlük saldırıları — bit flipping, IV manipulation, ECB cut-and-paste, CBC-MAC length extension, IV reuse
日本語の概要は準備中です。原文の説明を表示しています。
AES-GCM ve ChaCha20-Poly1305 nonce yeniden kullanımı saldırısı — GF(2^128) polinom kök bulma ile Hash Key kurtarma ve MAC sahteciliği.
日本語の概要は準備中です。原文の説明を表示しています。
tespit etmeabnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly Tespit.
日本語の概要は準備中です。原文の説明を表示しています。
Perform static and symbolic analysis of Solidity smart contracts using Slither and Mythril to tespit etmereentrancy, integer overflow, access control, and other vulnerability classes before Dağıt:ment to Ethereum mainnet.
日本語の概要は準備中です。原文の説明を表示しています。
Parses Kubernetes API server audit logs (JSON lines) to tespit etmeexec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat Tespit rules from audit event patterns. Use investigating yaparken Kubernetes cluster compromise or building k8s-specific SIEM Tespit rules.
日本語の概要は準備中です。原文の説明を表示しています。