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harness-delivery-loop

Guides agents through a file-backed Plan -> Work -> Review -> Release loop with explicit specs, Plans.md task contracts, verification evidence, independent review, and bounded long-running execution. Use for substantial SEOSONA work that must stay auditable across sessions or agents.

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LongLeo287/SEOSONA-OS
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4 de agosto de 2026 a las 05:01
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SKILL.md
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harness_delivery_loop
description
Guides agents through a file-backed Plan -> Work -> Review -> Release loop with explicit specs, Plans.md task contracts, verification evidence, independent review, and bounded long-running execution. Use for substantial SEOSONA work that must stay auditable across sessions or agents.
# Harness Delivery Loop ## Overview Use this skill when chat memory is not enough. The loop converts a request into durable files, bounded work slices, independent review, and evidence packages that survive context resets. ## When To Use - Multi-step implementation or ingestion work. - Work that may span sessions. - Work with high risk of scope drift. - Work where review must be separate from implementation. Do not use this for one-line fixes or simple questions. ## Operating Loop 1. Investigate. - Read the current repository state. - Reuse existing memory and KI items. - Keep unobserved facts marked as `unknown`. 2. Plan. - Write or update a spec when behavior, architecture, or scope is non-trivial. - Write or update `Plans.md` or an equivalent task checklist. - Include acceptance criteria, dependencies, stop conditions, and validation commands. 3. Work. - Execute one approved slice at a time. - Keep edits scoped to the active task. - Add tests or evidence proportional to risk. 4. Verify. - Run focused tests first. - Run broader gates when shared behavior, routing, or publication surfaces change. - Record exact commands and outcomes. 5. Review. - Review separately from implementation. - Check spec alignment, plan alignment, security, regressions, UX, and test evidence. - Major findings block completion. 6. Release or handoff. - Package evidence. - Commit/push only when explicitly requested or already authorized by the task. - Log memory/KI updates for durable learning. ## Long-Running Loop For long-running work: 1. Reload the active plan at each re-entry. 2. Resolve the next incomplete task. 3. Verify the task contract before acting. 4. Recover memory/context. 5. Execute a bounded slice. 6. Record evidence and status. 7. Continue, schedule, or stop based on the plan. ## Common Rationalizations | Rationalization | Reality | | --- | --- | | "The plan is in the conversation." | Chat context is volatile; file-backed plans are inspectable and resumable. | | "Review can happen at the end." | Late review makes scope drift expensive. | | "Unknown probably means absent." | Unknown means not observed; do not promote it to a claim. | | "One big pass is faster." | Bounded slices reduce rollback cost and make verification meaningful. | ## Red Flags - Work starts before the task boundary is clear. - Plans list tasks without acceptance criteria. - Verification evidence is reconstructed from memory. - Review approves changes without command output or file references. - The agent pushes changes without explicit publish intent. ## Verification - [ ] Current scope is recorded. - [ ] Each completed task has evidence. - [ ] Review is separate from implementation. - [ ] Remaining risks are explicit. - [ ] Memory logging was run for major outcomes.
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