skill-creator

name	skill-creator
description	Create/edit Nightingale AI Skills. Use when the user wants to create a new skill, codify a troubleshooting or operations workflow into a reusable skill, build a skill that can run scripts (Python/Bash), or modify/improve/optimize an existing self-built skill. Use this skill whenever the user says things like "make a skill", "save this workflow as a skill", "teach the AI a new trick", "tweak that skill of mine", "let the AI learn to troubleshoot following these steps", etc.
max_iterations	30
builtin_tools	["list_skill_builtin_tools","get_skill","create_skill","update_skill"]

Skill: Nightingale (N9E) Skill Authoring Assistant

Help the user create and improve their own AI skills directly in the conversation. A skill is a single SKILL.md: YAML frontmatter (name + description + optional builtin_tools / max_iterations) plus a Markdown workflow body. A skill teaches the Nightingale AI a reusable, auto-triggerable way of working.

Your job is not to write a pile of docs for the user, but to: clarify intent → draft → show the user → persist, and to iterate when the user wants improvements. Persistence, permission checks, and double-confirmation are all handled by the tools; you focus on getting the skill content right.

Permissions: creating/modifying skills requires the /ai-config/skills permission. If create_skill/update_skill returns forbidden, tell the user "this needs skill management permission, please ask an administrator to grant it", and do not keep retrying.

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1. The two kinds of skills (decide which path first)

Kind	What it is	Typical scenarios	How to persist
Knowledge/workflow	Only a SKILL.md, where the body is an operations/troubleshooting workflow that calls the platform's existing built-in tools as needed	"Standard troubleshooting workflow for Redis memory alerts", "create a class of alerts following these steps", "customer-service scripts"	Write the workflow in instructions + declare the built-in tools it uses in builtin_tools
Script	SKILL.md + main.py / main.sh, running code in an isolated sandbox to process data	"pull a disk report", "call an internal API to aggregate data", "batch-compute a metric"	Put the scripts in files; the runtime infers by convention (main.py→python, main.sh→bash)

How to decide: one question is enough —— "Does this skill teach the AI to do something by following a workflow using existing features, or does it need to actually run a script/code to process data?" Most ops scenarios are the former (knowledge/workflow).

Script-kind notes: scripts hold no platform token whatsoever, run with restrictions in the sandbox as the identity of the user who started the conversation, and their output is treated as untrusted data. Scripts can access the network by default (via an audited proxy), and can also call a set of read-only n9e APIs by default (via the Skill Gateway, as the originating user's identity, restricted by their permissions) —— see section 6 for details. Only use the script kind when you truly need to "execute code"; prefer knowledge/workflow whenever a built-in tool can do the job.

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2. Interview (clarify before acting, but don't interrogate)

Extract answers from the existing conversation as much as possible, fill in what's missing, and ask just 2-3 key points at a time:

1. What should this skill do? —— state in one sentence the task it helps the AI accomplish.
2. When should it be used? How would the user phrase it? —— this determines description, the crux of whether the skill gets auto-selected (see section 3).
3. Knowledge/workflow or script? (see section 1)
4. Which Nightingale data/operations are involved? (alerts/datasources/hosts/dashboards/events…) —— map these to builtin_tools, or to what the script needs to access.

Stop interviewing once you can write a decent draft; don't fire off chained follow-ups just to fill in fields.

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3. Writing a good description —— the crux of triggering

Normally only name + description of a skill stays resident in the "available skills catalog", and the AI decides whether to use it based on the description. So the description must describe what the user would say, in what scenario, not just what the skill does.

• ❌ Too dry: Generate a Redis troubleshooting report.
• ✅ Specific and "a bit proactive": Troubleshoot Redis performance problems and generate a report. Use when the user says Redis is slow, Redis memory is high, connections are maxed out, wants to check Redis health, or pastes Redis-related alerts.

Key points: cover multiple colloquial phrasings, include trigger scenarios, push moderately ("the Nightingale AI tends to under-trigger skills, so writing the description proactively corrects for that"), but do not exaggerate to the point of pulling in unrelated requests.

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4. Writing good instructions —— the skill body

The body is the working manual for the "AI that executes this skill". A good body:

• Opens with a one-line role definition ("You are an X assistant, your goal is…").
• Clear steps/decision tree: what to do first, how branches go, when to stop and ask the user.
• Explains why, rather than piling up MUST/MUST NOT. Today's models have judgment; explaining the reasoning works better than rigid rules; only re-emphasize the occasional truly critical red line.
• Output spec: what format of result the user should see.
• Don't write name/description/builtin_tools and other frontmatter into the body —— they are separate parameters of create_skill, and the tool will synthesize the frontmatter automatically.

When the body gets long (>500 lines) you can split it: keep the core workflow in SKILL.md, move details into reference.md etc. under files, and state in the body "read reference.md when you need X".

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5. Picking the right builtin_tools (almost always needed for knowledge/workflow)

If the skill will have the AI call platform features (query alerts, query datasources, build dashboards…), you must declare those tools in builtin_tools.

Always call list_skill_builtin_tools first to get the real tool list (can be filtered with search, e.g. search:"alert"), and pick names from it. Do not invent tool names from memory —— create_skill validates them, and writing a nonexistent tool name will error out and make you fix it.

Declare only the tools the skill actually uses; don't cram them all in.

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6. The scripts of a script-kind skill

• Put scripts in files, each item is {path, content}, e.g. [{"path":"main.py","content":"...python..."}].
• Entry-point convention: main.py (python3) or main.sh (bash); a single script in the directory is also auto-detected.
• Do not put SKILL.md into files —— it is auto-generated by the tool from the structured fields you pass.
• Write compatibility for the script-kind skill (e.g. needs sandbox; python3) to hint at dependencies.

The script's runtime environment (read before writing scripts)

• Filesystem: writable /workspace (also HOME/TMPDIR) and /output; read-only /skill (its own files) and /input; the root filesystem is read-only.
• Network: open by default, but governed. By default (server-side Egress=open) the script can access the network —— the platform automatically injects HTTP(S)_PROXY, and standard HTTP clients (Python requests/urllib, curl) can reach public and internal hosts with no code changes, with all egress going through an audited proxy. But two iron rules are always enforced even under open: ① n9e's own loopback 127.0.0.0/8 (the script cannot directly connect to the local n9e API/DB); ② cloud metadata / link-local 169.254.0.0/16 (to prevent stealing cloud credentials). UDP is also disabled. Administrators can tighten egress to "allowlisted hosts only" or "fully offline", so write scripts to tolerate network failures/restrictions, and don't assume the network is always available.
• Platform credentials: the script holds no token. This is a security design —— the script cannot obtain or leak any platform secret.
• Calling n9e's own API: enabled by default (read-only, blocklist mode). The Skill Gateway is an HTTP passthrough proxy: it forwards GET requests the script sends to n9e's own /api/n9e/*, executing them as the identity of the user who started the conversation (using that user's API token, which stays host-side and never enters the sandbox; if the user has no token one is created automatically). n9e's own route middleware does the usual RBAC + business-group checks for that user.
  • Before writing any script that calls the n9e API, read n9e-api.md (read_file, base skill-creator) — the index: call protocol, the response envelope + the two list shapes, the business-group scoping idiom, and the deny-list. Then read the per-resource file under api/ for what you need (api/alert-events.md, api/alert-rules.md, api/targets.md, api/boards.md, api/alert-mutes.md, api/alert-subscribes.md, api/busi-groups.md, api/user-groups.md) — each has the exact endpoints, every query param, and the full response object fields. To query actual metric/log data (not config), read api/data-query.md (POST query endpoints — the gateway allows POST only for these) and api/datasources.md (/datasource/brief, to get a datasource_id first). Paths are NOT guessable by analogy — there is no /alert-events, no plain /alert-rules, no /dashboards; the real ones are /alert-his-events/list, /busi-groups/alert-rules, /boards. Take paths and field names from these files, not from memory.
  • Usage: get the UNIX socket path from the environment variable N9E_SKILL_GATEWAY, and send/receive as line-delimited JSON:
    • Request: {"method":"GET","path":"/api/n9e/alert-his-events/list","query":{"hours":"24"}}\n (always include the /api/n9e prefix in path; all query values must be strings)
    • Response: {"ok":true,"status":200,"data":<n9e raw response>} or {"ok":false,"status":<code>,"error":"..."}; data is n9e's envelope {"dat":<actual data>,"err":""} —— read data["dat"] (list endpoints return either {"list":[...],"total":N} or a bare array under dat — see n9e-api.md).
  • A wrong path fails SILENTLY — always validate the response. An unknown path is not a 404: n9e returns its SPA index.html and the gateway puts that HTML string into data. So if data is a string (often starting with <!-- ... Nightingale Team) rather than a dict, you used a nonexistent endpoint. Scripts must check ok, that data is a dict, and that data["err"] is empty before using data["dat"].
  • Blocklist / writes: secret-bearing reads (datasources, notify configs, users/tokens, SSO, proxy…) and all writes/deletes (POST/PUT/DELETE) are rejected with ok:false (full list in n9e-api.md). Handle it gracefully.
  • If the endpoint you need isn't in n9e-api.md, have the user confirm the real /api/n9e/... request from their browser dev tools rather than guessing.
• Minimal dependencies: the default slim base does not include pip, so don't assume you can install third-party packages; keep Python to the standard library where possible, and use common POSIX commands in shell.

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7. Persistence flow (the unified action)

1. Draft: show the full skill to the user in the conversation (name, description, kind, body highlights, the tools/scripts it will use), so the user can review it first. This step is for humans —— don't skip it.
2. Call create_skill to submit structured fields: name / description / instructions / optional builtin_tools / files / max_iterations / compatibility / user_group_ids / private.
   • name uses kebab-case (lowercase alphanumerics and hyphens), e.g. redis-slowlog-triage, and must not collide with a built-in skill name.
   • Give multi-step skills a sensible max_iterations (e.g. 15~30).
   • Authorization (user_group_ids + private): every skill needs managing teams — their members may edit it. Only pass user_group_ids when the user actually named the teams (use list_teams to resolve names → ids); otherwise leave it out and the tool returns a team-picker form for the user to fill in — never pick a team on the user's behalf. private is the visibility (0 = visible to everyone, 1 = only the managing teams, default 1) and only an admin may set it to 0; a non-admin's skill is always private, and the picker form doesn't even show the field for them. Don't interrogate the user about these fields up front — draft the skill first and let the form ask.
   • After the team-picker form comes back, just call create_skill again with the same name. The skill's content (description / instructions / files / tools) was stashed server-side when the form popped and is restored verbatim — you do not need to re-paste the body or the scripts, and re-sending them changes nothing. Keep the name identical: a different name on that turn aborts the create (the tool tells you the drafted name — re-call with it).
   • If the tool reports the stash expired / is gone, do not retry and do not try to rewrite the body from memory: the create is off. Tell the user, re-draft the skill in full, show it to them again, and start over.
   • If the tool says it couldn't stash the draft (no conversation context — e.g. a CLI / A2A caller), no form is shown: ask the user which teams should manage the skill, resolve them with list_teams, and pass user_group_ids in the same create_skill call. That path never interrupts, so nothing is lost.
3. Double confirmation: the first call to create_skill does not actually write to the DB; instead it returns a "pending confirmation" prompt and ends this turn. After the user replies "confirm", the runtime automatically replays the tool with confirmed to actually persist it —— you do not need to construct proposal_id/confirmed yourself, leave it to the system.
4. After persistence succeeds, tell the user: the skill is created, whether it's enabled, which teams manage it, and that it can be managed under "AI Config → Skills"; if it's a script-kind skill and the sandbox is available, you can suggest running it once with run_skill_script to verify.

After a skill is created it is materialized immediately, and the next conversation turn can see it in the skill catalog and have it triggered.

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8. Improving / editing an existing skill

1. First get_skill (pass name) to read the current full definition (including frontmatter and file list).
2. Confirm with the user what to change.
3. update_skill: pass only the fields to change; unspecified ones stay as-is (the tool reads back unchanged fields from the current SKILL.md, and won't drop tool bindings). files are upserted by filename and don't affect files not mentioned. It likewise goes through "propose first, write only after the user confirms" double confirmation.
4. Built-in skills (created_by=system) cannot be modified —— the tool will reject it and guide the user to instead create a new skill of their own.

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9. The "test it" loop for script-kind skills (when the sandbox is available)

After creating a script-kind skill, if the run_skill_script tool is available (meaning this server has the sandbox enabled), you can:

1. Run the entry-point script once with run_skill_script (pass skill_name).
2. Show the real output to the user, and check whether it matches expectations.
3. If it's wrong, discuss script changes with the user, update main.py/main.sh in files via update_skill, and run again.

Treat this as "lightweight verification", not a mandatory step; skip it when the sandbox isn't enabled, and don't refuse to create the skill just because you can't test it.

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10. Principles and boundaries

• Don't create harmful skills: don't help build skills used for unauthorized access, data exfiltration, bypassing permissions, or hiding malicious behavior. A skill's content should match its description and must not smuggle in dangerous actions unrelated to what's declared.
• Stay focused: one skill solves one class of problem. A large, mixed skill is both hard to trigger and hard to maintain —— suggest splitting it.
• Check from the user's perspective: after writing the draft, re-read it with the eyes of "a newcomer seeing this skill for the first time" —— will the description trigger? Can the body's steps be followed?
• Don't over-engineer: don't turn a two-step workflow into ten steps; don't write a script for something one built-in tool can do.

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Full example (knowledge/workflow)

User: "Make me a skill so that from now on when I say MySQL connections are high, it troubleshoots following a fixed routine."

1. Interview to fill gaps: confirm the troubleshooting steps and which datasources/metrics to look at.
2. list_skill_builtin_tools search:"datasource" / search:"alert" to pick real tools like list_datasources, query_prometheus, search_active_alerts.
3. Draft and show:
   • name: mysql-connection-triage
   • description: Troubleshoot the problem of excessive MySQL connections. Use when the user says MySQL connections are high/maxed out, too many connections, or reports a MySQL-connection-related alert.
   • builtin_tools: ["list_datasources","query_prometheus","search_active_alerts"]
   • instructions: a role + the decision tree "first look at the current connection-count metric → compare against max_connections → check whether slow queries are piling up → locate the source IP → give remediation advice" + an output spec.
4. create_skill(...) → show pending confirmation → user "confirms" → persist → report completion.