Codex Autoresearch

codex-autoresearch

Autonomous goal-directed iteration. Modify -> Verify -> Keep/Discard -> Repeat.

When Activated

1. Classify the request as loop, plan, debug, fix, security, ship, or exec, and parse any inline config from the prompt.
2. Load references/core-principles.md and references/structured-output-spec.md. For active execution modes (loop, debug, fix, security, ship, exec), also load references/runtime-hard-invariants.md.
3. Load only the additional references the current situation needs:
   • references/session-resume-protocol.md when resuming or controlling an existing run
   • references/environment-awareness.md before choosing hardware-sensitive work
   • references/interaction-wizard.md for every new interactive launch (loop, debug, fix, security, ship) before execution begins
   • references/results-logging.md only when debugging TSV/state semantics or helper behavior directly
4. Load the selected mode workflow reference plus only the detailed cross-cutting protocols that actually apply (lessons, pivot, health-check, parallel, web-search, hypothesis-perspectives).
5. Use the bundled helper scripts when stateful artifacts or runtime control are involved. Resolve them relative to the loaded skill bundle root (<skill-root>/scripts/...), not the target repo root. In the common repo-local install this means commands such as python3 .agents/skills/codex-autoresearch/scripts/autoresearch_init_run.py .... For repo-managed control-plane helpers (autoresearch_resume_check.py, autoresearch_launch_gate.py, autoresearch_resume_prompt.py, autoresearch_supervisor_status.py, autoresearch_runtime_ctl.py status/stop), prefer --repo <repo> and let the helper derive default artifact paths.
6. Execute the selected workflow exactly as written and produce the required structured output and artifacts.

Core Loop

1. Read the relevant context.
2. Define a mechanical success metric.
3. Establish a baseline.
4. Make one focused change.
5. Verify with a command.
6. Keep or discard the change.
7. Log the result.
8. Repeat.

CDO Scheduler Consumer

cdo may use codex-autoresearch as its long-running scheduler/runtime for adaptive deliberation runs.

Split of responsibility:

• cdo owns the epistemology:
  • turn composition
  • agent roles
  • evidence discipline
  • synthesis directives
  • anti-groupthink / debate / negotiate behavior
• codex-autoresearch owns the runtime loop:
  • foreground/background execution
  • run tags
  • scheduler counters
  • artifact persistence
  • pause/resume
  • health checks
  • pivot/refine escalation
  • checkpoint discipline
  • finalization gates

For CDO scheduler runs, reinterpret the generic loop:

one focused change per iteration
  -> one focused turn strategy per iteration

mechanical verification
  -> mechanical scheduler constraints + evidence-backed synthesis

keep/discard experiment
  -> promote/carry/discard hypotheses and tensions

Minimum scheduler state for CDO runs:

cdo_scheduler:
  mode: autoresearch
  run_tag: "cdo-{slug}"
  min_turns: 10
  min_total_agents: 50
  skills_per_agent: 2
  adaptive_turn_width: true
  turn_count: 0
  total_agents: 0
  unique_skills: []
  exit_eligible: false

Do not use this mode for ordinary one-shot CDO work. Use it when the run has hard breadth/depth KPIs or needs unattended pause/resume behavior.

Modes

Use Mode: <name> in the prompt to force a specific subworkflow.

Required Config

For the generic loop, the following fields are needed internally. Codex infers them from the user's natural language input and repo context, then fills gaps through guided conversation:

• Goal
• Scope
• Metric
• Direction
• Verify

Optional but recommended:

• Guard
• Iterations
• Run tag
• Stop condition

For every new interactive run, use the wizard contract in references/interaction-wizard.md.

Explicit Run Modes

• $codex-autoresearch is the only primary human-facing entrypoint.
• For a new interactive run, scan the repo, ask the confirmation questions, and require an explicit run-mode choice: foreground or background.
• If the user chooses foreground, keep the loop in the current Codex session. Use the shared helper scripts (autoresearch_init_run.py, autoresearch_record_iteration.py, autoresearch_select_parallel_batch.py, autoresearch_supervisor_status.py) and do not create launch/runtime control artifacts.
• If the user chooses background, call autoresearch_runtime_ctl.py launch to persist the confirmed launch manifest and start the detached runtime controller in one step. The runtime itself should execute non-interactive codex exec sessions with the generated runtime prompt supplied on stdin. This skill now defaults those detached sessions to danger_full_access (--dangerously-bypass-approvals-and-sandbox) unless the user explicitly asks for the sandboxed workspace_write path. If the mini-wizard outcome is "fresh start", call autoresearch_runtime_ctl.py launch --fresh-start so prior persistent run-control artifacts are archived as part of the same handoff.
• If the user resumes an existing interactive run in the other mode, synchronize autoresearch-state.json internally before continuing. Background start already performs that sync automatically before it relaunches nested Codex sessions; autoresearch_set_session_mode.py remains an internal/scripted recovery helper, not a normal user-facing step.
• Treat the repo where the run starts as the primary repo. Single-repo runs are the default. If the task truly spans multiple codebases, declare companion repos explicitly and give each repo its own scope instead of stuffing absolute paths into one mixed scope string.
• Foreground and background share the same experiment protocol, but they are mutually exclusive for a given repo/run. Never try to keep both modes active against the same research-results.tsv / autoresearch-state.json artifacts at the same time.
• For status, stop, or resume requests, stay on the same skill entry. status and stop apply to background runs only; foreground runs stay in the current session.
• exec remains the advanced / CI path. It is fully specified upfront and does not use the interactive handoff.

Hard Rules

1. Ask before act for new interactive launches. For loop, debug, fix, security, and ship, ALWAYS scan the repo and ask at least one round of clarifying questions before the run starts. Load and follow references/interaction-wizard.md for every new interactive launch. The launch wizard must include an explicit run-mode choice: foreground or background. exec mode is the exception: it is fully configured upfront and must not stop for a launch question.
2. Respect the chosen run mode after launch approval. In interactive modes, once the user says "go" (or equivalent: "start", "launch", or any clear approval), follow the selected run mode exactly. Foreground stays in the current session and must not call autoresearch_runtime_ctl.py launch. Background calls autoresearch_runtime_ctl.py launch, creating the confirmed launch manifest and detached runtime as a single script-level action. Detached sessions use the confirmed launch manifest's execution_policy; this skill defaults to danger_full_access unless the user explicitly asks for sandboxed workspace_write. If the chosen background path is a fresh start after recovery analysis, use autoresearch_runtime_ctl.py launch --fresh-start so stale persistent run-control artifacts are archived automatically. exec mode has no launch question; once safety checks pass, it begins immediately.
3. Never ask after the user approves the run. Once the user has approved go in either foreground or background mode, do not pause mid-run to ask anything -- not for clarification, not for confirmation, not for permission. If you encounter ambiguity during the loop, apply best practices and keep going. The user may be asleep.
4. Read all in-scope files before the first write.
5. One focused change per iteration.
6. Mechanical verification only.
7. Commit before verification, but in swarm-style repos assume the worktree may already be dirty. Treat git as checkpointing, not history gardening.
8. In checkpoint mode the default git sequence is git add ., git commit, git pull, git push.
9. Do not use rebase as part of the loop. Recover by new forward commits, not history rewriting.
10. Failed experiments are recorded as discard / crash in the results log, but they may remain in git history as checkpoints when the repo uses checkpoint mode.
11. Discard gains under 1% that add disproportionate complexity.
12. Unlimited runs by default unless the user explicitly asks for Iterations: N.
13. External ship actions (deploy, publish, release) must be confirmed during the pre-launch wizard phase. If not confirmed before launch, skip them and log as blocker.
14. Do not ask "should I continue?". Once launched, keep the chosen run mode active until interrupted or a hard blocker / configured terminal condition appears (see references/autonomous-loop-protocol.md Stop Conditions for the full definition).
15. During active execution, keep references/runtime-hard-invariants.md as the primary runtime checklist. Foreground's core persistent artifacts are research-results.tsv and autoresearch-state.json; lessons are helper-derived secondary output.
16. When stuck (3+ consecutive discards), use the PIVOT/REFINE escalation ladder from references/pivot-protocol.md instead of brute-force retrying.
17. Prefer the bundled helper scripts over hand-editing research-results.tsv, autoresearch-state.json, or runtime-control files. Always call them via the skill-bundle path (<skill-root>/scripts/...); never call bare scripts/autoresearch_*.py from the target repo root unless the skill bundle itself is actually installed there.
18. In exec mode, never leave repo-root autoresearch-state.json behind. If helper scripts need state, use the exec scratch path and explicitly clean it up before exit. When you use autoresearch_init_run.py --mode exec ... with the default repo-root artifact names, do not manually rename old research-results.tsv or autoresearch-state.json; the helper already archives them to the canonical research-results.prev.tsv and autoresearch-state.prev.json paths before it starts fresh.
19. After any context compaction event (the CLI warns about thread length and compaction), re-read references/runtime-hard-invariants.md, references/core-principles.md, and the selected mode workflow from disk before the next iteration. Do not rely on memory of those documents after compaction.
20. Every 10 iterations, perform the Protocol Fingerprint Check defined in references/runtime-hard-invariants.md. Use Phase 8.7 of references/autonomous-loop-protocol.md only for the detailed re-anchoring procedure. If any item fails, re-read all loaded runtime docs from disk before continuing.

Structured Output

Every mode should follow references/structured-output-spec.md.

Minimum requirement:

• for interactive and user-facing modes, print a setup summary before the loop starts,
• for interactive and user-facing modes, print progress updates during the loop,
• for interactive and user-facing modes, print a completion summary at the end,
• for exec, emit only the machine-readable JSON payloads defined in references/exec-workflow.md,
• write the mode-specific output files when the workflow defines an output directory.

Quick Start

$codex-autoresearch
I want to get rid of all the `any` types in my TypeScript code

$codex-autoresearch
I want to make our API faster but I don't know where to start

$codex-autoresearch
pytest is failing, 12 tests broken after the refactor

Codex scans the repo, asks targeted questions to clarify your intent, asks you to choose foreground or background for interactive runs, then starts the loop. You never need to write key-value config.

Git Philosophy Override

In swarm-style repos, dirty worktrees are normal. This skill should treat git as a checkpointing mechanism, not a pristine-history mechanism:

• assume the tree may already be dirty,
• checkpoint forward with git add ., git commit, git pull, git push,
• do not require rebase,
• do not rely on reset/revert as the normal loop primitive.

Multi-Run Model

This skill now supports multiple autoresearch runs in the same repo when each run has a distinct run_tag.

• Each run_tag gets its own artifact directory under .autoresearch/runs/<run-tag>/
• Background control files, TSV state, and lessons no longer need to collide at repo root
• status and stop should target a specific run via --run-tag
• Git checkpointing is still shared at the repo level, so concurrent runs must coordinate through the shared checkpoint lock at .autoresearch/checkpoint.lock

References

• references/core-principles.md
• references/runtime-hard-invariants.md
• references/loop-workflow.md
• references/autonomous-loop-protocol.md
• references/interaction-wizard.md
• references/structured-output-spec.md
• references/modes.md
• references/plan-workflow.md
• references/debug-workflow.md
• references/fix-workflow.md
• references/security-workflow.md
• references/ship-workflow.md
• references/exec-workflow.md
• references/results-logging.md
• references/lessons-protocol.md
• references/pivot-protocol.md
• references/web-search-protocol.md
• references/environment-awareness.md
• references/parallel-experiments-protocol.md
• references/session-resume-protocol.md
• references/health-check-protocol.md
• references/hypothesis-perspectives.md