Agent skill

Reflect

by sharpdeveye in sharpdeveye/maestro

Analyze command history to identify which skills work, which fail, and where to improve.

MITAuto-check passedMobile

Install Reflect

skills CLI
$ npx skills add sharpdeveye/maestro --skill reflect -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sharpdeveye/maestro reflect --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sharpdeveye/maestro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/source/skills/reflect .claude/skills/reflect && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reflect
GitHub stars
592
Token cost
~932 tokens
SKILL.md length
304 words
Files
1
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

Analyze command history to identify which skills work, which fail, and where to improve.

  • Works in 2 steps: .maestro/audit.jsonl — every command… → .maestro/decisions.jsonl — decisions…
  • Mobile work in your project
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Reflect is an agent skill from sharpdeveye/maestro. Analyze command history to identify which skills work, which fail, and where to improve.

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Mobile. The repository describes itself as: Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and… The licence is MIT.

When your agent uses it

  • Mobile work in your project

Example prompts

  • “/reflect”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. .maestro/audit.jsonl — every command invocation with duration, cost, and outcome
  2. .maestro/decisions.jsonl — decisions made with outcomes and next steps

What it can do on your machine

Read from SKILL.md and the folder at commit 00f9115. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reflect loads about 932 tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 304 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~932

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sharpdeveye/maestro at commit 00f9115, republished under its MIT licence (© sharpdeveye). 304 words, ~932 tokens.

Download SKILL.mdSave it as .claude/skills/reflect/SKILL.md (or your agent's skills folder).
name
reflect
description
Analyze command history to identify which skills work, which fail, and where to improve.
argument-hint
[time period]
category
analysis
version
2.0.0
user-invocable
true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.


Analyze the Maestro audit trail and decision log to produce a skill-effectiveness scorecard. This tells you which commands work, which fail, and where your workflow needs attention.

Data Sources

Read these files from the project root:

  1. .maestro/audit.jsonl — every command invocation with duration, cost, and outcome
  2. .maestro/decisions.jsonl — decisions made with outcomes and next steps

If neither file exists, respond: "No audit data found. Run commands with Maestro to start tracking, then come back."

Analysis Dimensions

1. Usage Frequency

  • Which commands run most/least?
  • Are any commands never used? (candidates for removal)

2. Completion Rate

  • What % of invocations complete successfully?
  • Which commands fail most often?

3. Command Flow

  • What are the most common command sequences (A → B)?
  • Which commands lead to follow-ups vs. abandonment?
  • Abandonment rate per command (no follow-up within 30 min)

4. Cost Distribution

  • Total estimated cost across all commands
  • Cost per command (average)
  • Most/least expensive commands

5. Duration Analysis

  • Average duration per command
  • Outliers (unusually slow invocations)
Output Format
text
╔══════════════════════════════════════════╗
║          MAESTRO EFFECTIVENESS           ║
╠══════════════════════════════════════════╣
║ Commands Run         __ (__ unique)      ║
║ Completion Rate      __%                 ║
║ Most Used            /_____ (__×)        ║
║ Most Abandoned       /_____ (__% ⚠️)     ║
║ Avg Duration         __s                 ║
║ Total Cost           ~$__.__             ║
╠══════════════════════════════════════════╣
║           STRONGEST PIPELINES            ║
╠══════════════════════════════════════════╣
║ /_____ → /_____    __×                   ║
║ /_____ → /_____    __×                   ║
╠══════════════════════════════════════════╣
║           COST PER COMMAND               ║
╠══════════════════════════════════════════╣
║ /_____    $__.__/run  ████░░  avg        ║
║ /_____    $__.__/run  █░░░░░  cheap      ║
║ /_____    $__.__/run  █████░  costly     ║
╚══════════════════════════════════════════╝

INSIGHTS:
1. [Data-driven observation with recommended action]
2. [Data-driven observation with recommended action]
3. [Data-driven observation with recommended action]
Insights Rules

Every insight MUST:

  • Reference specific data (e.g., "40% abandonment rate")
  • Suggest a specific Maestro command to address it
  • Distinguish correlation from causation
Reflection Checklist
  • All 5 analysis dimensions covered
  • Scorecard generated with real data
  • Insights are data-driven, not speculative
  • Cost estimates labeled as approximate (~)
  • Recommended actions reference specific Maestro commands

After reflecting, run /streamline to remove unused commands, or /refine on the most-abandoned command to improve its prompt quality.

NEVER:

  • Require audit data to exist — degrade gracefully
  • Invent metrics beyond what the logs contain
  • Show cost data without the "estimate" disclaimer (~)
  • Make judgments without evidence (say "100% completion rate" not "works great")
  • Compare across projects — reflect is project-scoped

© sharpdeveye, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in source/skills/reflect of sharpdeveye/maestro.

Open the folder on GitHubat commit 00f9115

Compare with similar skills

Reflect next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reflect compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reflect this skillsharpdeveye/maestro592—~932Automated safety check: PassMIT
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Swiftui Protwostraws/SwiftUI-Agent-Skill5.1k2 repos~1.5kAutomated safety check: PassMIT
Kortix Brandkortix-ai/suna20k—~4kAutomated safety check: PassCustom licence
Ip As LogoKartikLabhshetwar/better-shot2.4k1 repos~4.3kAutomated safety check: PassMIT
Compose Multiplatform Patternsmonta-app/ocpp-emulator1805 repos~2kAutomated safety check: PassApache-2.0

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Categories

Questions about Reflect

What does Reflect do?

Analyze command history to identify which skills work, which fail, and where to improve. Reflect is an agent skill from sharpdeveye/maestro. Analyze command history to identify which skills work, which fail, and where to improve.

When should I use Reflect?

Reflect fits situations like: mobile work in your project.

How do I install Reflect in Claude Code?

Run `npx skills add sharpdeveye/maestro --skill reflect -a claude-code`. Or copy the skill folder (source/skills/reflect in sharpdeveye/maestro) into .claude/skills/reflect in your project. Claude Code loads it when a task matches its description.

How do I install Reflect in Codex?

Run `npx skills add sharpdeveye/maestro --skill reflect -a codex`. Or copy the skill folder (source/skills/reflect in sharpdeveye/maestro) into .agents/skills/reflect in your project. Codex loads it when a task matches its description.

Can I use Reflect in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sharpdeveye/maestro --skill reflect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reflect, .gemini/skills/reflect, .github/skills/reflect and .opencode/skills/reflect in your project.

What does Reflect need to run?

SKILL.md names no scripts, command-line tools or credentials: Reflect is instructions for the agent only.

Does Reflect access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Reflect safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Reflect use?

Reflect is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reflect use?

About 932 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Reflect?

Skills that share tags, products or a category with Reflect: React Native Best Practices (vercel-labs/openreview, 1.7k stars), Swiftui Pro (twostraws/SwiftUI-Agent-Skill, 5.1k stars), Kortix Brand (kortix-ai/suna, 20k stars) and Ip As Logo (KartikLabhshetwar/better-shot, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reflect?

sharpdeveye (a GitHub user) maintains it in sharpdeveye/maestro, which has 592 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on April 29, 2026.

Source: sharpdeveye/maestro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.