# Reflection > Use when the user invokes $reflection or asks for an independent, concise post-task audit of whether Codex overthought a completed request, used too many tools, or should persist a project-scoped workflow lesson. - Skill: `jasoncodemaker/reflection` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add jasoncodemaker/reflection` - Raw SKILL.md: https://api.skillmd.com/api/skills/jasoncodemaker/reflection/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: JasonCodeMaker (https://skillmd.com/u/jasoncodemaker) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/jasoncodemaker/reflection --- # Reflection Audit the observable execution of completed tasks. Optimize for lower latency and fewer tool calls without removing work required for correctness, safety, or applicable instructions. ## Resolve the task - With no explicit scope, audit the substantive user request immediately before the reflection request and the complete Codex execution that answered it. - Treat clarifications and corrections made before that answer as part of the same task. - If the user names tasks or asks for the last N tasks, audit exactly that scope. - Do not count the reflection request itself as part of the task. - If no completed task is available, say so. Do not invent a trace. ## Gather evidence Use the inherited conversation and tool trace first. Review only observable evidence: requests, commentary, tool calls and outputs, file changes, checks, errors, retries, and the final answer. Never request or reconstruct hidden chain-of-thought. Do not open the raw session trace by default. Inspect it only when the user requests exact timing or tool counts, or when a material claim cannot be resolved from the inherited trace. Resolve the current session unambiguously; never assume the newest session file is the right one. If it cannot be resolved, mark the exact measurement as unknown. ## Delegate the audit Spawn exactly one independent subagent with: - model: `gpt-5.6-sol` - reasoning effort: `medium` - the smallest inherited turn range that contains the full task trace - a prompt containing the scope, raw observable evidence, and the report contract below Do not give the subagent the main agent's diagnosis or suggested findings. Tell the subagent to: - remain read-only and make no external changes; - spawn no further agents; - avoid tools unless one targeted read is essential to interpret evidence already in the trace; - compare effort with task complexity; - distinguish avoidable work from checks required by safety, correctness, user instructions, `AGENTS.md`, or an active skill; - identify root causes rather than isolated symptoms; - answer in the user's language. If an independent subagent is unavailable, fail closed and explain that an independent reflection could not be produced. Do not substitute the main agent's self-review. ## Return the report Return the subagent's findings after checking only for unsupported claims, secrets, and scope violations. Do not run a second audit. Keep the report to roughly 300 to 500 Chinese characters, or comparable length in another language, using this structure: ```markdown ## 结论 合理 | 轻度过度 | 严重过度。One sentence relating task complexity to execution cost. ## 核心浪费 - Observable evidence -> root cause -> shortest better path. ``` Include at most three waste findings. Then add: ```markdown ## 不可削减项 - Work that was justified by correctness, safety, or binding instructions. ## 可固化规则 - 当 时，应 ；除非 。 ``` Include at most three persistence candidates. Do not use numeric scores. If the trace contains no material inefficiency: - write `未发现核心浪费` under `核心浪费`; - write `未发现值得固化的问题` under `可固化规则`; - do not turn successful behavior into generic persistence candidates. ## Persist only on explicit request `$reflection` is read-only. Do not change memory, skills, `AGENTS.md`, configuration, or project files unless the user explicitly asks to persist or promote a selected finding. ### Project memory When the user explicitly asks to persist a finding: 1. Resolve the exact project root with `git rev-parse --show-toplevel`. If there is no project root, ask for the intended scope before writing. 2. Search the current ARC Rule/Learning state, memory registry, and pending ad-hoc notes once for an equivalent rule. If one already covers it, report that and do not duplicate it. 3. If the project has a versioned `.research/VERSION` and one committed Project, call the installed `research-op/scripts/record_project_learning.py` helper with the selected observation, candidate rule, applicable ARC actions, owning skills, and package scope. This writes a non-binding ARC Learning with a content-addressed NOTE EvidenceRef. Do not also write an ad-hoc memory note. 4. Otherwise, write one small update note named `-.md` through the supported memory-update mechanism. On the local Codex filesystem, use `${CODEX_HOME:-$HOME/.codex}/memories/extensions/ad_hoc/notes/`. 5. Bind an ad-hoc fallback rule to the exact project root: ```markdown # Project reflection rule: scope: applies_to: cwd= observed_pattern: rule: When , ; unless . evidence: ``` Persist only the selected reusable rule. Do not store the full report, raw transcript, secrets, volatile metrics, or one-off frustration. Never hand-edit generated memory summaries, registries, ARC state, or projections. Report the ARC Learning id and note reference, or the ad-hoc note path. Do not claim that background consolidation has completed unless it was verified. ### Stronger enforcement Project memory is a recall layer. If the same problem recurs or the user explicitly asks to enforce it, recommend the narrowest durable owner: - nearest applicable `AGENTS.md` for mandatory project behavior; - the owning skill for a reusable workflow rule; - an explicitly admitted ARC Project Rule for governed cross-session context; - a test or hook for deterministic enforcement. Do not modify any of these surfaces without an explicit user request.