fi1osof.ru

Это странно, потому что в целом он работает, но почему-то сломался именно в ловабл-компоненте. Кидает ошибку.

```text
Runtime Error
Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: undefined. You likely forgot to export your component from the file it's defined in, or you might have mixed up default and named imports.
```

Figure out why Markdown is breaking on the server

There are a lot of old pages in the history. We either need to find the old website in our archives and restore the old content, or clean them out. The first option is, ofowel, preferable.

Update information in Yandex.Webmaster

This is already implemented on the backend, we just need to add it to the interface

Add the ability to create subtasks

На странице задачи вывести список таймеров

Update the logo and launch a minor rebranding.

Need to add .nullable() to the schema, because right now, if nothing has been entered yet, saving results in projectId can not be null

Fix task creation without a project

Need to double-check Google and Yandex Webmaster in more detail. Most likely, the queue is simply clogged with old pages that have long been lost from the site. That is, we need to let them know that these pages don't need to be searched for, and that new pages need to be indexed.&#x20;

Investigate why the website is poorly indexed

Display project information on the task page

Starting the timer should automatically move the task to the In Progress status

Draft version:



```ts
 
type FetchWithProgress = (
  input: RequestInfo | URL,
  init?: RequestInit & { onUploadProgress?: (percent: number) => void },
) => Promise<Response>

const fetchWithUploadProgress: FetchWithProgress = (input, init) => {
  const onProgress = init?.onUploadProgress

  if (typeof window === 'undefined' || !onProgress || !init?.body) {
    return fetch(input, init)
  }

  return new Promise<Response>((resolve, reject) => {
    const xhr = new XMLHttpRequest()
    xhr.open(init.method || 'POST', input.toString())

    if (init.headers) {
      const headers = init.headers as Record<string, string>
      Object.entries(headers).forEach(([key, value]) => {
        xhr.setRequestHeader(key, value)
      })
    }

    xhr.upload.onprogress = (event) => {
      if (event.lengthComputable) {
        const percent = Math.round((event.loaded / event.total) * 100)
        onProgress(percent)
      }
    }

    xhr.onload = () => {
      resolve(
        new Response(xhr.responseText, {
          status: xhr.status,
          statusText: xhr.statusText,
        }),
      )
    }

    xhr.onerror = () => reject(new Error('Network error'))
    xhr.onabort = () => reject(new DOMException('Aborted', 'AbortError'))

    if (init.signal) {
      init.signal.addEventListener('abort', () => xhr.abort())
    }

    xhr.send(init.body as XMLHttpRequestBodyInit)
  })
}


```

Add upload progress to apollo-client

Overall, all the functionality is already there from the haih-agent, we just need to expose it and style it a bit.

Add Blog

Configure Varnish caching server

# Goal

Bring AI collaboration to the next level: the user defines a **working goal** in natural language, for example:

> "Find out why project X is not working"

After that, the agent independently goes through the process from finding the project and gathering context to testing hypotheses and recording the results in fi1osof.ru.

## Target Scenario

Example of the desired workflow:

1. Find the project by name or ID.
2. Get its current context.
3. Read related tasks.
4. Read recent worklogs.
5. View recent events/changes for the project.
6. Check website/API/services availability, if applicable.
7. Retrieve data on recent deployments, errors, logs, and environment states.
8. Formulate several hypotheses for the cause of the problem.
9. Test the hypotheses that can be checked with available tools.
10. Create a new task or update an existing one.
11. Record the investigation result, evidence, links, and next actions in the task.

Roughly:

```text
find project
→ gather recent changes
→ view open tasks
→ read recent worklogs
→ check website/API availability
→ view errors and deployments
→ formulate hypotheses
→ test available hypotheses
→ create/update task
→ record investigation result
```

# What Already Exists

The GraphQL API already provides the basic entities needed to start such a process:

- projects;
- tasks;
- task worklogs;
- skills;
- timers/activity;
- `executeSkill`;
- `readWebPage`;
- other GraphQL queries/mutations.

This already makes it possible to implement part of the agent loop without separate external orchestration.

# What is Missing

## 1. Standardized Technical Project Context

It should be possible to unambiguously retrieve technical sources related to the project:

- repositories;
- services;
- production/staging/dev environments;
- public and internal URLs;
- API endpoints;
- deployments;
- CI/CD runs;
- logs;
- errors;
- monitoring;
- configuration;
- documentation.

Without this, the agent can analyze tasks and work history well, but is not always able to determine the technical root cause of an incident.

## 2. Explicit Entity Relationships

It is desirable to have a machine-readable relationship graph, for example:

```text
Project
├─ Repository
├─ Service
│  ├─ Environment
│  ├─ Endpoint
│  ├─ Deployment
│  └─ LogSource
├─ Task
│  └─ TaskWorkLog
└─ Documentation
```

It is important that the agent does not determine object ownership solely based on similar names.

## 3. Activity Feed / Change History

A unified way is needed to answer the question:

> "What changed right before the problem occurred?"

Useful events:

- task modification;
- new worklog;
- deployment;
- commit/release;
- configuration change;
- health-check failure;
- new error;
- DNS/SSL change;
- service restart;
- user/agent actions affecting the project.

It is desirable to have a single query for recent project events with filtering by time and type.

## 4. Secure Hypothesis Testing Tools

The agent needs read-only or controlled diagnostic actions, such as:

- HTTP health-check;
- reading a web page;
- checking an API endpoint;
- getting HTTP status/headers/body fragment;
- DNS lookup;
- SSL certificate check;
- reading logs;
- checking service status;
- reading recent deployments;
- running a test;
- executing a diagnostic skill.

Part of this can be built on top of existing `readWebPage` and `executeSkill`.

## 5. Autonomy Policy

It is necessary to explicitly define which actions the agent can perform autonomously.

### Allowed Without Additional Confirmation

- read data;
- search for a project and related entities;
- perform read-only diagnostics;
- formulate hypotheses;
- read logs and monitoring;
- create a diagnostic worklog;
- create a regular task if it is a natural result of the investigation;
- supplement the task with diagnostic results.

### Only Upon Explicit User Command

- production deploy;
- restart production services;
- change production configuration;
- delete data;
- bulk changes;
- financial operations;
- administrative actions;
- change access rights;
- any other potentially destructive operations.

# High-Level Skills

Not only low-level skills like "get list of tasks" are needed, but also working-goal level procedures.

Possible skills:

- `investigate_project`
- `check_project_health`
- `analyze_recent_failures`
- `summarize_project_state`
- `investigate_incident`
- `analyze_recent_changes`

For example, `investigate_project` can define a reproducible procedure:

```text
resolve project
→ collect context
→ collect recent activity
→ inspect tasks/worklogs
→ run health checks
→ inspect deployments/errors/logs
→ produce hypotheses
→ test hypotheses
→ persist findings
```

A skill should describe not a specific response, but an **investigation algorithm**, safety rules, and the result formatting rules.

# Investigation Result Format

It is desirable to save the result in `Task.content` or a worklog in Markdown.

Example structure:

```markdown
# Investigation Result

## Symptom
...

## What Was Checked
- ...
- ...

## Observations
- ...

## Hypotheses
1. ...
2. ...

## Confirmed Cause
...

## Evidence
- [log / URL / deployment / commit](...)

## What Was Done
- ...

## Next Actions
- [ ] ...
- [ ] ...
```

# Important Task Storage Principle

For tasks in fi1osof.ru, observe the separation:

- `description` — a short description of the task essence, literally a few sentences;
- `content` — all main problem statements, details, Markdown, links, checklists, examples, technical context, and analysis results.

Do not use `description` as the main long task field.

# First Practical Stage

Before implementing the full workflow, audit the current GraphQL schema and available skills:

1. Determine which of the listed data sources already exist.
2. Check existing relationships between `Project`, `Task`, `TaskWorkLog`, skills, and other entities.
3. Find available operations for web/API diagnostics.
4. Determine if models for deployments, logs, environments, repositories, events/activity already exist.
5. Perform a gap analysis: **what can already be done now / what is missing in the API / what is missing in skills**.
6. Based on this, design a minimal `investigate_project` workflow.

# Definition of Done

A task can be considered implemented when a command like:

> "Find out why haih.net is not working today"

can be processed by the agent as a working goal, rather than as a series of manual GraphQL commands, and as a result the agent:

- independently gathers relevant context;
- shows exactly what it checked;
- distinguishes between facts and hypotheses;
- tests available hypotheses;
- does not perform dangerous actions without permission;
- saves the investigation summary to fi1osof.ru.

Design and implement a secure workflow where an AI independently gathers project context, tests hypotheses, and records investigation results based on a single goal.

Agentic Project Troubleshooting Mode

# Problem

Currently, the roles of task participants are not clearly visible enough in the task interface. It is important for the user to immediately understand:

- who created the task;
- who is responsible for its execution.

This is especially critical in human-AI agent collaboration, as well as in teams where the creator and the executor are often different people.

# What needs to be done

Add the display of two separate roles:

- **Creator** — the user/agent who created or assigned the task;
- **Assignee** — the user/agent to whom the task is assigned.

# Where to display

## Task list

Each row/card in the list should display:

- the creator;
- the assignee.

The display should be compact and not overload the list.

## Task card

In the detailed task card, both roles must be displayed explicitly and separately, with clear labels.

# What to check

- which GraphQL fields already contain creator / author / owner / assignee;
- whether there is a difference between the actual task creator and the task author/assigner;
- whether users and AI agents are displayed correctly;
- what happens if no assignee is set;
- what happens if the creator and the assignee are the same user;
- to ensure the roles are not merged into a single interface field.

# Expected behavior

When opening the task list or task card, the user immediately understands without extra actions:

> who created the task and who is responsible for it.

# Definition of Done

- both roles are visible in the task list;
- both roles are visible in the task card;
- the creator and assignee are displayed as separate entities;
- both regular users and AI agents are correctly supported;
- an unassigned task clearly indicates that, e.g., "Unassigned".

The task interface needs to explicitly display both the creator and the assignee in both the task list and the specific task card.

Tasks

Show creator and assignee in the task list and task card