Borrowing it
Nothing to install: this file belongs to zkysar1/Claude-Mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zkysar1/Claude-Mind/main/.claude/skills/reflect/SKILL.mdgit clone --depth 1 https://github.com/zkysar1/Claude-MindWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zkysar1/claude-mind/reflect)<a href="https://agentmods.dev/skills/zkysar1/claude-mind/reflect"><img src="https://agentmods.dev/badge/skills/zkysar1/claude-mind/reflect.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.13286 |
| Opus 5 | $0.00052 | $0.06643 |
| Sonnet 5 | $0.00021 | $0.02657 |
| Haiku 4.5 | $0.00010 | $0.01329 |
Grade A, and why
reflect 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 840 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/reflect — Reflexion-Based Self-Learning Engine
Generates structured reflections from hypothesis outcomes, extracts reusable strategies, tracks violations of expectation, and synthesizes hierarchical insights. This is the core self-learning mechanism — it turns raw outcomes into institutional knowledge.
Based on: Reflexion (Shinn 2023), ABC Method, Generative Agents (Park 2023), VoE metacognitive framework.
Quick Links
| Sub-skill | Mode | Purpose |
|---|---|---|
| /reflect-on-outcome | --on-hypothesis, --on-execution, --batch-micro |
Outcome reflection: hypothesis ABC chains, execution patterns, batch micro |
| /reflect-on-self | --extract-patterns, --calibration-check |
Self-model: pattern synthesis, strategy extraction, calibration |
| /reflect-maintain | --curate-memory, --curate-aspirations |
Maintenance: memory curation, aspiration grooming |
| /reflect-tree-update | (shared protocol) | Propagate tree changes upward |
Related skills: /replay (hippocampal replay), /aspirations-spark (Phase 6.5 immediate learning)
Parameters
--on-hypothesis <hypothesis-id>— Reflect on a single resolved hypothesis (session/short/long horizon)--on-execution— Reflect on a goal execution outcome (pattern signatures, contradiction detection, investigation goals)--batch-micro— Batch-reflect on micro-hypotheses from working memory (session-end)--extract-patterns— Mine all resolved hypotheses for reusable strategies--calibration-check— Analyze confidence calibration across all hypotheses--full-cycle— Run all reflection modes in sequence (includes --batch-micro)--curate-memory— Retire stale/low-utilization strategies, guardrails, reasoning bank entries, and pattern signatures--curate-aspirations— Groom stuck goals whose evidence has converged (backlog grooming)--level N— Reflection depth (0=episode, 1=pattern, 2=strategic). Default: auto-detect
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.
- 4d ago Changed 131f86ea556c
- 8d ago First seen · 840 lines · 104 tokens per session scan A 9eb23139bab6
reflect is a skill published in the GitHub repository zkysar1/Claude-Mind (5 stars, last pushed 2d ago), licensed MIT. It adds 104 tokens to every session and 13,286 once invoked, about $0.0005 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.
Other skills, from other repositories
hive.context-preservation
Proactively extract critical values from tool results into working notes before automatic context pruning destroys them.
hive.note-taking
Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
learning-capture
Log corrections, errors, and discoveries to orbital/LESSONS.md so future sessions avoid repeating mistakes.
obsidian
A skill for working with an Obsidian vault, which is a folder of Markdown notes used as a personal knowledge base. It covers creating, extending, searching, and organizing notes, including links, tags, folders, and daily entries.
paper-research
Do not use for general web literature reviews outside ArXiv, thesis coaching, homework tutoring, or long-term research memory management. Do not save a research log unless the user explicitly asks to save one.