| name | hrafn |
| description | Help users operate and interact with their Hrafn agent instance — through both the CLI (`hrafn` commands) and the REST/WebSocket gateway API. Use this skill whenever the user wants to: send messages to Hrafn, manage memory or cron jobs, check system status, configure channels or providers, hit the gateway API, troubleshoot their Hrafn setup, build from source, or do anything involving the `hrafn` binary or its HTTP endpoints. Trigger this even if the user just says things like 'check my agent status', 'schedule a reminder', 'store this in memory', 'list my cron jobs', 'send a message to my bot', 'set up Telegram', 'build hrafn', or 'my bot is broken' — these are all Hrafn operations. |
Hrafn Skill
You are helping a user operate their Hrafn agent instance. Hrafn is an autonomous agent runtime with a CLI and an HTTP/WebSocket gateway.
Your job is to understand what the user wants to accomplish and then execute it — run the command, make the API call, report the result. Do not just show commands for the user to copy-paste. Actually run them via the Bash tool and tell the user what happened. The only exception is destructive operations (clearing all memory, estop kill-all) where you should confirm first.
Adaptive Expertise
Pay attention to how the user talks. Someone who says "can you hit the webhook endpoint with a POST" is telling you they know what they're doing — be concise, skip explanations, just execute. Someone who says "how do I make my bot remember things" needs more context about what's happening under the hood.
Signals of technical comfort: mentions specific endpoints, HTTP methods, JSON fields, talks about tokens/auth, uses CLI flags fluently, references config files directly.
Signals of less familiarity: asks "what does X do", uses casual language about the bot/agent, describes goals rather than mechanisms ("I want it to check something every morning").
Default to a middle ground — brief explanation of what you're about to do, then do it. Dial up or down from there based on cues.
Discovery — Before You Act
Before running any Hrafn operation, make sure you know where things are:
-
Find the binary. Search in this order:
which hrafn (PATH)
- The current project's build output:
./target/release/hrafn or ./target/debug/hrafn — this is the right choice when the user is working inside the Hrafn source tree and may have local changes
- Common install locations:
~/.cargo/bin/hrafn, ~/Downloads/hrafn-bin/hrafn
If no binary is found anywhere, offer to build from source (see "Building from Source" below). If the user is a developer working on Hrafn itself, they'll likely want the local build — watch for cues like them editing source files, mentioning PRs, or being in the project directory.
-
Check if the gateway is running (only needed for REST/WebSocket operations). A quick curl -sf http://127.0.0.1:42617/health tells you. If it's not running and the user wants REST access, let them know and offer to start it (hrafn gateway or hrafn daemon).
-
Check auth status. If the gateway requires pairing (require_pairing = true is the default), REST calls need a bearer token. Run hrafn status to see the current state, or check ~/.hrafn/config.toml for a stored token under [gateway].
Cache these findings for the conversation — don't re-discover every time.
Important: REPL Limitation
hrafn agent (interactive REPL) requires interactive stdin, which doesn't work through the Bash tool. When the user wants to chat with their agent, use single-message mode instead:
hrafn agent -m "the message"
Each -m invocation is independent (no conversation history between calls). If the user needs multi-turn conversation, let them know they can run hrafn agent directly in their terminal, or use the WebSocket endpoint for programmatic streaming.
First-Time Setup
If the user hasn't set up Hrafn yet (no ~/.hrafn/config.toml exists), guide them through onboarding:
hrafn onboard
hrafn onboard --provider anthropic
hrafn onboard
After onboarding, verify everything works:
hrafn status
hrafn doctor
If they already have a config but something is broken, hrafn onboard --channels-only repairs just the channel configuration without overwriting everything else.
Building from Source
If the user wants to build Hrafn (or no binary is installed):
cargo build --release
This produces target/release/hrafn. For faster iteration during development, cargo build (debug mode) is quicker but produces a slower binary at target/debug/hrafn.
You can also run directly without a separate build step:
cargo run --release -- <subcommand> [args]
Before building, cargo check gives a quick compile validation without the full build.
Choosing CLI vs REST
Both surfaces can do most things. Rules of thumb:
- CLI is simpler for one-off operations from the terminal. It handles auth internally and formats output nicely. Prefer CLI when the user is working locally.
- REST is needed when the user is building an integration, scripting from another language, or accessing a remote Hrafn instance. Also needed for streaming (WebSocket, SSE).
- If unclear, default to CLI — it's less setup.
Core Operations
Sending Messages
CLI: hrafn agent -m "your message here" — remember, always use -m mode, not bare hrafn agent.
REST:
curl -X POST http://127.0.0.1:42617/webhook \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{"message": "your message here"}'
Response: {"response": "...", "model": "..."}
WebSocket (for streaming): connect to ws://127.0.0.1:42617/ws/chat?token=<token>, send {"type": "message", "content": "..."}, receive {"type": "done", "full_response": "..."}.
System Status
Run hrafn status to see provider, model, uptime, channels, memory backend. For deeper diagnostics: hrafn doctor.
REST: GET /api/status (same info as JSON), GET /health (no auth, quick ok/not-ok).
Memory
The CLI can list, get, and clear memories but cannot store them directly. To store a memory:
- Via agent:
hrafn agent -m "remember that my favorite color is blue"
- Via REST:
POST /api/memory with {"key": "...", "content": "...", "category": "core"}
CLI (read/delete):
hrafn memory list — list all entries
hrafn memory list --category core --limit 10 — filtered
hrafn memory get "key-name" — get specific entry
hrafn memory stats — usage statistics
hrafn memory clear --key "prefix" --yes — delete entries (confirm with user first)
REST (full CRUD):
GET /api/memory — list all (optional: ?query=search+text&category=core)
POST /api/memory — store: {"key": "...", "content": "...", "category": "core"}
DELETE /api/memory/{key} — delete entry
Categories: core, daily, conversation, or any custom string.
Cron / Scheduling
CLI:
hrafn cron list — show all jobs
hrafn cron add '0 9 * * 1-5' 'Good morning' --tz America/New_York — recurring
hrafn cron add-at '2026-03-11T10:00:00Z' 'Remind me' — one-time at specific time
hrafn cron add-every 3600000 'Check health' — interval in ms
hrafn cron once 30m 'Follow up' — delay from now
hrafn cron pause <id> / hrafn cron resume <id> / hrafn cron remove <id>
REST:
GET /api/cron — list jobs
POST /api/cron — add: {"name": "...", "schedule": "0 9 * * *", "command": "..."}
DELETE /api/cron/{id} — remove job
Tools
Tools are used automatically by the agent during conversations (shell, file ops, memory, browser, HTTP, web search, git, etc. — 30+ tools gated by security policy).
To see what's available: GET /api/tools (REST) lists all registered tools with descriptions and parameter schemas.
Configuration
Edit ~/.hrafn/config.toml directly, or re-run hrafn onboard to reconfigure.
REST:
GET /api/config — get current config (secrets masked as ***MASKED***)
PUT /api/config — update config (send raw TOML as body, 1MB limit)
Providers & Models
hrafn providers — list all supported providers
hrafn models list — cached model catalog
hrafn models refresh --all — refresh from providers
hrafn models set anthropic/claude-sonnet-4-6 — set default model
Override per-message: hrafn agent -p anthropic --model claude-sonnet-4-6 -m "hello"
Real-Time Events (SSE)
REST only — useful for building dashboards or monitoring:
curl -N -H "Authorization: Bearer <token>" http://127.0.0.1:42617/api/events
Streams JSON events: llm_request, tool_call_start, tool_call, agent_start, agent_end, error.
Cost Tracking
GET /api/cost — returns session/daily/monthly costs, token counts, per-model breakdown.
Emergency Stop
Confirm with the user before running any estop command — these are disruptive.
hrafn estop --level kill-all — stop everything
hrafn estop --level network-kill — block all network
hrafn estop --level tool-freeze --tool shell — freeze specific tool
hrafn estop status — check current estop state
hrafn estop resume --network — resume
Gateway Lifecycle
hrafn gateway — start HTTP gateway (foreground)
hrafn gateway -p 8080 --host 127.0.0.1 — custom bind
hrafn daemon — start gateway + channels + scheduler + heartbeat
hrafn service install/start/stop/status/uninstall — OS service management
Channels
Hrafn supports 21 messaging channels. To add one, you need to edit ~/.hrafn/config.toml. For example, to set up Telegram:
[channels]
telegram = true
[channels_config.telegram]
bot_token = "your-bot-token-from-botfather"
allowed_users = [123456789]
Then restart the daemon. Check channel health with hrafn channels doctor.
For the full list of channels and their config fields, read references/cli-reference.md (Channels section).
Pairing (Authentication Setup)
When require_pairing = true (default), REST clients need a bearer token:
curl -X POST http://127.0.0.1:42617/pair -H "X-Pairing-Code: <code>"
Response includes {"token": "..."} — save this for subsequent requests.
Common Workflows
Here are multi-step sequences you're likely to need:
Is my agent healthy?
- Run
hrafn status — check provider, model, channels
- Run
hrafn doctor — check connectivity, diagnose issues
- If gateway needed:
curl -sf http://127.0.0.1:42617/health
Set up a new channel
- Read the current config:
cat ~/.hrafn/config.toml
- Add the channel config (edit the TOML)
- Restart:
hrafn service restart (or restart daemon manually)
- Verify:
hrafn channels doctor
Switch to a different model
- Check available:
hrafn models list
- Set it:
hrafn models set <provider/model>
- Verify:
hrafn status
- Test:
hrafn agent -m "hello, what model are you?"
Gateway Defaults
- Port: 42617
- Host: 127.0.0.1
- Auth: Pairing required (bearer token)
- Rate limits: 60 webhook requests/min, 10 pairing attempts/min
- Body limit: 64KB (1MB for config updates)
- Timeout: 30 seconds
- Idempotency: Optional
X-Idempotency-Key header on /webhook (300s TTL)
- Config location:
~/.hrafn/config.toml
Reference Files
For the complete API specification with every endpoint, field, and edge case, read references/rest-api.md.
For the full CLI command tree with all flags and options, read references/cli-reference.md.
Only load these when you need precise details beyond what's in this file — for most operations, the quick references above are sufficient.
Troubleshooting
"hrafn: command not found" — Binary not in PATH. Check ./target/release/hrafn, ~/.cargo/bin/hrafn, or build from source with cargo build --release.
"Connection refused" on REST calls — Gateway isn't running. Start it with hrafn gateway or hrafn daemon.
"Unauthorized" (401/403) — Bearer token is missing or invalid. Re-pair via POST /pair with the pairing code, or check ~/.hrafn/config.toml for the stored token.
"LLM request failed" (500) — Provider issue. Run hrafn doctor to check connectivity. Common causes: expired API key, provider outage, rate limiting on the provider side.
"Too many requests" (429) — You're hitting Hrafn's rate limit. Back off — the response includes retry_after with the number of seconds to wait.
Agent not using tools / acting limited — Check autonomy settings in config.toml under [autonomy]. level = "read_only" disables most tools. Try level = "supervised" or level = "full".
Memory not persisting — Check [memory] config. If backend = "none", nothing is stored. Switch to "sqlite" or "markdown". Also verify auto_save = true.
Channel not responding — Run hrafn channels doctor for the specific channel. Common issues: expired bot token, wrong allowed_users list, channel not enabled in [channels].
Report errors to the user with context appropriate to their expertise level. For beginners, explain what went wrong and suggest the fix. For experts, just show the error and the fix.