Reflect

Community

sneg55

reflect

Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.

Overview

Publisher	sneg55
Repository	`agent-starter`
Skill name	`reflect`
Stars	74
Forks	4
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by sneg55 on GitHub. Read the source before you install it.

Installation

Install the Reflect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/sneg55/agent-starter/tree/main/skills/reflect
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/sneg55/agent-starter.git /tmp/agent-starter
mkdir -p .claude/skills
cp -r /tmp/agent-starter/skills/reflect .claude/skills/reflect

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reflect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reflect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reflect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Reflect: Per-Project Self-Improvement

You are performing a reflection - turning the signal this project has captured into durable improvements. Nothing is applied without the developer's approval.

Phase 1 - Orient (gather signal)

Run the stats script over the ledger: ~/.claude/hooks/harness-ledger-stats.sh --ledger .harness/ledger.jsonl --min-recurr 3 If that path doesn't exist, the hooks aren't installed at ~/.claude/hooks/ - run the copy from wherever this project keeps them. If the script prints all zeros there is no ledger yet (no signal captured) - stop; there is nothing to reflect on.
Read the current project rules in CLAUDE.md so you improve them rather than duplicate.
Read recent feedback-type memory files - these are the developer's explicit corrections and are the highest-value signal. Locate the memory directory first (it sits next to MEMORY.md; find . -name MEMORY.md if unsure), then grep -l 'type: feedback' <memory-dir>/*.md.
Read the most recent .harness/reflections/*.md report (if any) to recall the last metric snapshot and what was already changed.

Phase 2 - Cluster

From the stats output and feedback memories, identify recurring problems:

Each recurring <rule> <prefix> <count> line is a friction cluster - the same check keeps firing in the same area.
Group related feedback corrections by theme.
Ignore one-off events; focus on what repeats.

Phase 3 - Propose (one candidate per cluster)

For each cluster, draft exactly one proposed change, choosing the fitting type:

Type	When	Where it lands
Project rule	a convention would stop the repeat	append to `CLAUDE.md` project-specific section
Threshold change	a guardrail is too strict/loose	a diff to `.claude/settings.json` or the hook - shown, never auto-applied
Lint rule	the mistake is mechanically catchable	a diff to `eslint.config.mjs` / `biome.jsonc`
ADR / knowledge	durable "why" worth keeping	a new memory file or `docs/adr/` note

Present all proposals together as a numbered list with the concrete change for each.

Phase 4 - Gate & Record

Ask the developer to approve, edit, or reject each proposal (like /remember).
Apply only the approved ones, then commit them (use /commit).
Create .harness/reflections/ if needed (mkdir -p .harness/reflections), then write a reflection report to .harness/reflections/YYYY-MM-DD.md containing:
- the full stats output (the metric snapshot, so the next reflection can compare),
- the clusters you found,
- which proposals were approved / rejected and why.

The report is committed; the raw .harness/ledger.jsonl stays gitignored. Signal is private; wisdom is shared.

Measuring success

The headline metric is recurring_events from the stats output. Compare it to the value in the previous reflection report. If a rule you promoted last time worked, the cluster it targeted should have shrunk. Note the trend explicitly in the new report.

Frequently asked questions

What does the Reflect AI skill do?

Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.

Why use Reflect on TypingMind?

Because you install it once and use it with any model. Reflect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reflect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sneg55/agent-starter/tree/main/skills/reflect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reflect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reflect?

As many as you like. As long as a model supports skills, you can use Reflect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reflect AI skill free?

Yes. It is published on GitHub by sneg55 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Reflect: Per-Project Self-Improvement

Phase 1 - Orient (gather signal)

Phase 2 - Cluster

Phase 3 - Propose (one candidate per cluster)

Phase 4 - Gate & Record

Measuring success

Frequently asked questions

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