session-reflection

A process for reviewing a completed work session and updating the project's saved plans, tasks, approvals, and notes.

In plain words
What is it for?
Use it when closing substantial work or when asked to reflect on a session and reconcile what happened with the project's durable records.
Why use it?
It prevents decisions, unresolved issues, and ownership details from being lost when a session ends.

Skill for Claude CodeCodex

Part of the gaia plugin — 38 skills, 9 agents, 11 hooks shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/metraton/gaia/session-reflection
Any agent
npx skills add metraton/gaia --skill session-reflection
Clone the repo
git clone --depth 1 https://github.com/metraton/gaia

Made for: Claude Code, Codex.

Or install gaia, the plugin that ships this one along with the rest of its 38 skills, 9 agents, 11 hooks.

Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,443 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00061 $0.01443
Opus 5 $0.00030 $0.00722
Sonnet 5 $0.00012 $0.00289
Haiku 4.5 $0.00006 $0.00144

Measured 3d ago against content hash 7a46288f7a22, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

session-reflection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/session-reflection/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Session Reflection

Session reflection is everything the session lived, contrasted against what belongs to each project, until no loop is left dangling. It recovers the whole arc, reconciles it against the durable corpus in both directions, and leaves every item — settled or open — with an owner a later session can find.

Upstream are the transcript, the specialist contracts, and the injected digest. Downstream is memory curation: reflection ends when the corpus is correct. Compaction is a separate act, performed only when the user asks for it.

Process

  1. Recover the whole arc. Scan from the session opening for accepted and rejected proposals, deferrals, closures, user corrections, and specialist reactions — recency is not weight, an early settled choice still stands. Include cross_layer_impacts, open_gaps, and failure_report findings even where the user never reacted: they are observations about Gaia, not conversational agreements. reference.md holds the full recovery pass.
  2. Reconcile in both directions. For each initiative the session touched, read its live corpus — gaia memory get-relevant --initiative=<key> returns that whole pending set uncapped, with bodies — and ask: what did this session produce with no home yet, and what already-open pending did it close, advance, or invalidate? A topic search only answers whether your own phrasing has a row; the closure you owe is usually phrased in terms that predate the session that solved it. Read briefs, plans, tasks, and approvals the same way — a conversation cannot close an object the substrate still shows open. A session that produced any Gaia improvement touched gaia_system by definition, whatever project it was nominally working on — sweep it too. reference.md holds the reverse-sweep mechanics and the objective-state checks that verify a SKIP.
  3. Classify disjointly. Separate settled decisions and learnings, genuinely open work, and Gaia improvements. When closure is uncertain, classify as open; a lost pending costs more than an extra review. For anything about Gaia itself, memory/SKILL.md's one-line test decides the initiative — gaia_system for an observed failure or friction, project-scoped gaia for a decision to build or change something.
  4. Give every item a home, and know what the home does. The pending worklist that returns to the user each session selects class=thread with status carry_forward or open only — an anchor still reaches a dispatched agent as held knowledge, but never comes back as work. Filing live work as an anchor hides it; filing settled knowledge as a thread turns the worklist into noise. SKIP is a home only when you name the canonical object that owns the item — already-canonical work is referenced, never copied.
  5. Adjudicate against the exception boundary. The boundary table lives in memory/SKILL.md ("Process" step 5) — this is a pointer to it, not a copy. Most operations are decided and executed directly; only the rows the boundary marks "ask first" or "veto" wait on the user.
  6. Run the curation, closures included. Materialize the step 2 closures alongside the new rows; reclassify and append are non-mutative, so nothing but omission keeps a resolved thread open. A closing arc that passes the milestone test in reference.md uses checkpoint, one atomic write; an ordinary close does not.
  7. State the resume point. One line naming what the next session picks up — a pointer, not a container: everything it names already has a row.

Read the full file on GitHub · 121 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 121 lines · 0 tokens per session scan A 7a46288f7a22

Subscribe to this mod's changes

session-reflection is a skill published in the GitHub repository metraton/gaia (3 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 1,443 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

python-feature-lifecycle

Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.

microsoft/agent-framework · 43 tokens

python-development

Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.

microsoft/agent-framework · 35 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

reflect

Review recent work, find repeated workflow patterns, and suggest reusable skills, agents, commands, config changes, or playbooks. Use when the user asks to learn from past sessions, improve recurring workflows, or identify what should be turned into reusable agent instructions.

alvinunreal/oh-my-opencode-slim · 53 tokens

codemap

Generate comprehensive hierarchical codemaps for UNFAMILIAR repositories. Expensive operation - only use when explicitly asked for codebase documentation or initial repository mapping.

alvinunreal/oh-my-opencode-slim · 34 tokens

length-converter

Convert between common length units (miles, km, feet, meters) using a multiplication factor.

microsoft/agent-framework · 24 tokens