Deli Autoresearch

<!-- zhiyuan_AutoResearch: protocol framework for long-horizon autonomous tasks. Ships no executable code; prescribes conventions for state persistence, stall detection, and layered guardians. -->

zhiyuan_AutoResearch

This skill is a protocol framework for long-horizon autonomous tasks (days to weeks). It ships no executable code; instead it prescribes a set of battle-tested conventions: how state is persisted, how stalls are detected, how guardians are layered, and what constraints bind agent behavior. Implementation details are left to the adopter's environment.

0. Runtime Mapping (this environment)

The protocol's abstract mechanisms map onto concrete tools here:

State files and logs below are tool-agnostic: they are what makes every iteration resumable.

Controlled Shortcut Completion

When this skill is selected through the Academic Research sidebar shortcut, the runtime applies the Deep Research evidence gate in addition to this protocol. Before requesting completion, save the final cited research report as a .md deliverable and a readable .md, .txt, or .json validation report in the selected workspace. Record both with workflow_state; plans, delegates, and source URLs are necessary evidence but never substitute for the final report.

1. Motivation

Long-running code agents exhibit three recurring failure modes:

1. Cognitive loop — successive iterations try similar directions with diminishing returns, unable to escape a local optimum on their own.
2. Stalling — the agent finishes a chunk of work, outputs a summary, and waits for user feedback. Externally the session looks alive and polling runs, but work has effectively stopped. Run logs show this is more common than crashes.
3. Runtime fragility — context compaction silently breaks the loop; closing a session takes down the timers parasitic on it. Failures go unnoticed by default.

The common cause of all three is missing engineering scaffolding, not insufficient model capability. Every mechanism in this framework targets the failure modes above.

2. Behavioral Constraints

1. Zero interaction — no prompting the user during a run: no Plan Mode, no question tool, no ending on a question. Continue working until the user stops you. Resolve ambiguity yourself and write the reasoning to the log (level=decision).
2. Ready means execute — the most common hidden violation: finishing all preparation and then asking "should I submit?". The purpose of preparation is execution; submitting, resubmitting, fixing, and starting monitors are all routine operations needing no confirmation.
3. Callback means report-alive — after context compaction the loop dies silently. The first action of every callback is to update its own last_seen, then check liveness; on detecting failure it restarts immediately and logs it.
4. Persist state to files — all progress is written to state/ files, not conversation memory. Each iteration starts a fresh session, injecting only curated state; never use resume.
5. Guardian / worker separation — a heartbeat patrol may take only three actions on tasks that are not its own: liveness-check, restart, nudge. It does not read their data, modify their state files, or report to the user on their behalf.

3. Architecture

┌── Orchestrator (current session / durable cron) ──┐
│ monitor state files → detect stalls → inject direction │
└────┬─────────────┬─────────────┬────────────┘
  [Task A]      [Task B]      [Task C]   ← each its own fresh session

Core design decisions:

• Separate execution from evaluation — the agent doing the work does not judge its own progress; stall determination is made by the orchestration layer based on quantitative metrics.
• Fresh session over resume — context accumulation is the primary cause of cognitive loops. Each iteration starts with fresh context; state is injected via files.
• Enforced direction diversity — before each iteration, read the list of tried directions; a new direction must differ from all history.

4. State Files

{task}/state/
├── task_spec.md           # goal / milestones / success criteria
├── progress.json          # {iteration, total_findings, status, stale_count}
├── findings.jsonl         # accumulated findings (append-only)
├── directions_tried.json  # directions already tried
└── iteration_log.jsonl    # per-iteration summary

{task}/logs/
├── work.jsonl             # written by work agent; decisions tagged level=decision
├── orchestrator.jsonl     # written by orchestrator
└── heartbeat.jsonl        # written by heartbeat watchdog

Log line format: {"ts":"...", "source":"...", "level":"info|warn|error|decision", "event":"...", "detail":"..."}

5. Usage

# 1. Initialize the task directory, write state/task_spec.md and an initial progress.json

# 2. Start the orchestrator loop with the agent_loop tool (goal mode):
#    goal = "every task's progress.json advances each iteration".
#    Each iteration: (1) read progress.json for all tasks;
#    (2) if stale_count>=3 generate a fresh direction; (3) launch a work agent
#    via the subagent tool (with explicit goal and completion criteria);
#    (4) write results back to state files; (5) call agent_loop next.
#    Zero interaction.

# 3. Register a durable SQLite-backed scheduled-task heartbeat watchdog
# (survives across sessions):
# hourly patrol: write a timestamp; check each loop's last_seen against interval×3,
# restart if exceeded; check each task's progress for stalls over 2h, nudge if stalled.
# Zero interaction. In Work sessions, agent_loop itself is the cadence — keep
# iterations short enough that a stalled one is visible in the session.

6. Stall Detection & Pivoting

"Pivot structure, not tactics" comes from practice: when a task stalls repeatedly within a frame, the decisive gain usually comes from correcting the environment/structural constraint itself, not from tuning strategy parameters harder inside the existing frame.