hermes-labyrinth-observability
Read-only observability dashboard plugin for Hermes Agent — journeys, crossings, guideposts, and reports.
What this skill does
# Hermes Labyrinth
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Hermes Labyrinth is a **read-only observability dashboard plugin** for [Hermes Agent](https://github.com/NousResearch/hermes-agent). It turns autonomous agent runs into a navigable map of **crossings** (prompts, tool calls, tool results, failures, model switches, subagents, approvals, memory hits, redactions, context compression, cron runs) with exportable evidence. It is not a chat UI — it is a black-box recorder for agents moving through unknown work.
---
## Install
### Plugin Directory Install
```bash
mkdir -p ~/.hermes/plugins
git clone https://github.com/stainlu/hermes-labyrinth.git ~/.hermes/plugins/hermes-labyrinth
```
Start or restart the Hermes dashboard:
```bash
hermes dashboard
```
If the dashboard is already running, rescan plugins without restarting:
```bash
curl http://127.0.0.1:9119/api/dashboard/plugins/rescan
```
Open the dashboard in your browser and select the **Labyrinth** tab.
### Optional Theme
```bash
mkdir -p ~/.hermes/dashboard-themes
cp ~/.hermes/plugins/hermes-labyrinth/theme/hermes-labyrinth.yaml ~/.hermes/dashboard-themes/
```
---
## What Labyrinth Tracks
| View | Contents |
|---|---|
| **Journey index** | Recent CLI, dashboard, gateway, cron, and delegated work |
| **Labyrinth map** | Ordered crossings through a selected agent journey |
| **Inspector** | Input, output, duration, status, evidence, guideposts per crossing |
| **Guideposts** | Generated observations backed by local evidence |
| **Skill atlas** | Bundled, optional, external, and user skill inventory |
| **Cron gate** | Scheduled autonomy, next runs, last failures, workdirs |
| **Model ferry** | Model/provider transitions across sessions |
| **Reports** | Redacted Markdown and JSON exports for one journey |
---
## API Surface
All endpoints are read-only. The plugin API is served by Hermes dashboard at:
```
http://127.0.0.1:9119/api/plugins/hermes-labyrinth/
```
### Endpoints
```
GET /api/plugins/hermes-labyrinth/health
GET /api/plugins/hermes-labyrinth/journeys
GET /api/plugins/hermes-labyrinth/journeys/{journey_id}
GET /api/plugins/hermes-labyrinth/journeys/{journey_id}/crossings
GET /api/plugins/hermes-labyrinth/skills
GET /api/plugins/hermes-labyrinth/cron
GET /api/plugins/hermes-labyrinth/guideposts
GET /api/plugins/hermes-labyrinth/reports/{journey_id}.json
GET /api/plugins/hermes-labyrinth/reports/{journey_id}.md
```
### Example: Fetch All Journeys
```bash
curl http://127.0.0.1:9119/api/plugins/hermes-labyrinth/journeys | jq .
```
### Example: Fetch Crossings for a Journey
```bash
JOURNEY_ID="your-journey-id"
curl "http://127.0.0.1:9119/api/plugins/hermes-labyrinth/journeys/${JOURNEY_ID}/crossings" | jq .
```
### Example: Export a Journey Report as Markdown
```bash
JOURNEY_ID="your-journey-id"
curl "http://127.0.0.1:9119/api/plugins/hermes-labyrinth/reports/${JOURNEY_ID}.md" > report.md
```
### Example: Export a Journey Report as JSON
```bash
JOURNEY_ID="your-journey-id"
curl "http://127.0.0.1:9119/api/plugins/hermes-labyrinth/reports/${JOURNEY_ID}.json" > report.json
```
### Example: Health Check
```bash
curl http://127.0.0.1:9119/api/plugins/hermes-labyrinth/health
```
---
## Python API Client Examples
The plugin backend lives at `dashboard/plugin_api.py`. You can also call the HTTP API from any language. Here are Python examples:
```python
import urllib.request
import json
BASE = "http://127.0.0.1:9119/api/plugins/hermes-labyrinth"
def get_journeys():
with urllib.request.urlopen(f"{BASE}/journeys") as r:
return json.loads(r.read())
def get_crossings(journey_id: str):
with urllib.request.urlopen(f"{BASE}/journeys/{journey_id}/crossings") as r:
return json.loads(r.read())
def get_report_json(journey_id: str):
with urllib.request.urlopen(f"{BASE}/reports/{journey_id}.json") as r:
return json.loads(r.read())
def get_report_md(journey_id: str) -> str:
with urllib.request.urlopen(f"{BASE}/reports/{journey_id}.md") as r:
return r.read().decode("utf-8")
# Usage
journeys = get_journeys()
for j in journeys:
print(j["id"], j.get("status"), j.get("started_at"))
```
### Iterate Crossings and Inspect Tool Calls
```python
import urllib.request
import json
BASE = "http://127.0.0.1:9119/api/plugins/hermes-labyrinth"
def inspect_tool_crossings(journey_id: str):
with urllib.request.urlopen(f"{BASE}/journeys/{journey_id}/crossings") as r:
crossings = json.loads(r.read())
for crossing in crossings:
if crossing.get("type") == "tool_call":
print(f"Tool: {crossing['tool']}")
print(f" Status: {crossing.get('status')}")
print(f" Duration: {crossing.get('duration_ms')}ms")
print(f" Input: {json.dumps(crossing.get('input', {}))[:200]}")
print()
inspect_tool_crossings("your-journey-id")
```
### Download and Save All Reports for Recent Journeys
```python
import urllib.request
import json
import pathlib
BASE = "http://127.0.0.1:9119/api/plugins/hermes-labyrinth"
OUT = pathlib.Path("./labyrinth-reports")
OUT.mkdir(exist_ok=True)
with urllib.request.urlopen(f"{BASE}/journeys") as r:
journeys = json.loads(r.read())
for j in journeys[:10]: # last 10 journeys
jid = j["id"]
try:
with urllib.request.urlopen(f"{BASE}/reports/{jid}.json") as r:
(OUT / f"{jid}.json").write_bytes(r.read())
with urllib.request.urlopen(f"{BASE}/reports/{jid}.md") as r:
(OUT / f"{jid}.md").write_bytes(r.read())
print(f"Saved reports for {jid}")
except Exception as e:
print(f"Failed {jid}: {e}")
```
---
## JavaScript / Frontend API Examples
The frontend plugin bundle lives in `dashboard/dist/`. If you're extending the UI or writing a custom integration:
```javascript
const BASE = "http://127.0.0.1:9119/api/plugins/hermes-labyrinth";
async function fetchJourneys() {
const res = await fetch(`${BASE}/journeys`);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
return res.json();
}
async function fetchCrossings(journeyId) {
const res = await fetch(`${BASE}/journeys/${journeyId}/crossings`);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
return res.json();
}
async function fetchReportMarkdown(journeyId) {
const res = await fetch(`${BASE}/reports/${journeyId}.md`);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
return res.text();
}
// Example: log all failed crossings in the most recent journey
async function logFailures() {
const journeys = await fetchJourneys();
if (!journeys.length) return;
const crossings = await fetchCrossings(journeys[0].id);
const failed = crossings.filter(c => c.status === "failure" || c.status === "error");
console.table(failed.map(c => ({
type: c.type,
tool: c.tool ?? "-",
duration_ms: c.duration_ms,
error: c.error?.slice(0, 120),
})));
}
logFailures();
```
---
## Build & Development
The frontend is built from `src/parts/*.js` + `src/labyrinth.css` into `dashboard/dist/`. The demo `index.html` is generated with content-hash query strings.
```bash
# Build dashboard/dist and index.html
npm run build
# Run reproducibility and parse checks
npm run check
# Run browser smoke tests (headless Chrome)
npm run smoke
# Smoke-test the deployed GitHub Pages demo
npm run smoke:live
# Run all tests (build checks, fixture tests, smoke)
npm test
```
### Full Test Suite
```bash
npm test
```
Runs:
- Reproducible build checks for `dashboard/dist` and `index.html`
- Frontend JavaScript parse checks
- Backend Python parse checks
- API normalization fixture tests (including numeric Hermes timestamps)
- Packed-artifact and dead-control regressions
- Headless Chrome smoke coverage for map modes, route changes, search, dataset switching, and threshold filter
---
## Repository Layout
```
.
├── dashboard/
│ ├── manifest.json # Hermes dashboard plugin manifest
│ ├── plugin_apRelated in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.