agent-loop-ext
Crash-resilient external agent loop with state persistence and CI/CD integration
Launch an AIWG Mission — durable, audited dynamic agent orchestration toward a completion criterion. AIWG owns the conductor (activity-log, gates, best-output, checkpoint/resume, cost); native primitives drive worker mechanism. Surfaces as /aiwg-mission in Codex (AIWG-owned, no
$ npx -y skills add jmagly/aiwg --skill aiwg-mission --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/aiwg-missionContext preview
The summary Claude sees to decide when to auto-load this skill.
Launch an AIWG Mission — durable, audited dynamic agent orchestration toward a completion criterion. AIWG owns the conductor (activity-log, gates, best-output, checkpoint/resume, cost); native primitives drive worker mechanism. Surfaces as /aiwg-mission in Codex (AIWG-owned, no
namespace: aiwg name: aiwg-mission platforms: [all] kernel: true description: Launch an AIWG Mission — durable, audited dynamic agent orchestration toward a completion criterion. AIWG owns the conductor (activity-log, gates, best-output, checkpoint/resume, cost); native primitives drive worker mechanism. Surfaces as /aiwg-mission in Codex (AIWG-owned, no plugin dependency).
You are launching (or participating in) an **AIWG Mission** — AIWG's dynamic, durable, audited agent orchestration. A Mission decomposes a goal into worker cycles, runs them toward a measurable completion criterion, and aggregates the result, while AIWG owns the bookkeeping and gates regardless of which agent stack each worker runs on.
This is an **AIWG-specific kernel capability**. On Codex it surfaces as `/aiwg-mission`, deployed by AIWG to `~/.codex/prompts/` — it is **AIWG-owned**, not the plugin-provided `/workflow` an arbitrary Codex install may or may not have (`/workflow` is not a core Codex primitive — see `.aiwg/research/provider-workflow-integration.md`).
Natural-language triggers:
Non-triggers (route elsewhere):
1. **State the completion criterion measurably** (per the `vague-discretion` rule). "good enough" is not a criterion; "all tests pass and CI green", "score ≥ 85", "every flagged finding verified" are. If the user's goal is vague, extract a checkable criterion first.
2. **Right-size + decompose.** Break the goal into independently-verifiable worker cycles (`subagent-scoping`). Decide single-stack vs cross-stack:
3. **Dispatch.** For durable/detached/unattended Missions use AIWG's external route (`aiwg mc dispatch` / `ralph-external`) so the Mission survives the session ending. For in-session orchestration, the native primitive may drive the *mechanism* — but AIWG still owns everything in step 4.
4. **Retain ownership (non-negotiable, identical across stacks).** Whatever drives the worker mechanism, AIWG owns:
structural-variant quota, hard budget stop, and private eval/holdout channels where applicable
5. **Control the loss surface.** For any long-running, budgeted, or eval-driven Mission, declare observable iteration/wall-clock/token/tool/spend ceilings before dispatch. Each retry records the hypothesis, expected failure mode, distinguishing diagnostic, and whether the next attempt is structurally different. If the budget or exploration quota is exhausted, stop and emit a best-output report; do not continue by random walk. Eval/holdout Missions expose only aggregate score/probe/status or VOID to workers, while holdout answers and detailed lint diagnostics stay private. For durable Mission Control dispatch, use `--max-iterations`, `--max-total-tokens`, `--max-output-tokens`, `--max-tool-calls`, `--max-total-cost`, `--max-wall-clock-minutes`, and `--exploration-quota`.
6. **Converge + report.** Run cycles until the completion criterion is met (with a `max-cycles` escape hatch), select the best output, and report what each worker did and the aggregated result. Apply the anti-laziness recovery protocol (PAUSE→DIAGNOSE→ADAPT→RETRY→ESCALATE) rather than abandoning on failure.
Before hand-rolling a Mission, run `aiwg discover "<the goal>"` — a curated Flow or skill may already exist (the `skill-discovery` rule). A Mission is for genuinely *dynamic* orchestration where no pre-established Flow fits.
Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.
Repo: jmagly/aiwg
Crash-resilient external agent loop with state persistence and CI/CD integration
Detect requests for iterative autonomous agent loops and route to the appropriate loop executor
Automatically execute tests when code-generating agents modify source files, enforcing the execute-before-return pattern
Enable agent loops to learn from similar past tasks and share patterns across loops
Query and manage the executable feedback debug memory
Execute tests on generated code and iterate until passing