Borrowing it
Nothing to install: this file belongs to onurpolat05/ALBA. 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/onurpolat05/ALBA/main/.claude/skills/reflect/SKILL.mdgit clone --depth 1 https://github.com/onurpolat05/ALBAWrote 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/onurpolat05/alba/reflect)<a href="https://agentmods.dev/skills/onurpolat05/alba/reflect"><img src="https://agentmods.dev/badge/skills/onurpolat05/alba/reflect.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00910 |
| Opus 5 | $0.00052 | $0.00455 |
| Sonnet 5 | $0.00021 | $0.00182 |
| Haiku 4.5 | $0.00010 | $0.00091 |
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 7d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ALBA Reflect
Analyze recent session logs, errors, and learnings to find patterns and suggest improvements.
When to Use
- After several work sessions (weekly recommended)
- When the same problems keep appearing
- To discover workflow improvements
- To propose new rules based on observed patterns
Why background: false
Since Claude Code v2.1.218, a skill with context: fork defaults to background: true — the fork
detaches and its report shows up later as a notification, not in the turn you asked. Reflection is
a conversation starter: the report is only useful if you can immediately say "yes, create that
rule". So this skill pins background: false. The fork still absorbs the cost of reading every
daily log, but the finished report comes back inline and Step 4 below happens in the main
conversation, where you can approve actions.
Requires Claude Code v2.1.218 or later; older versions ignore the field and already behave this way.
Process
Step 1: Gather Data
Read recent files. Default window is the last 7 days or last 5 sessions; if $ARGUMENTS holds a
number, use that many days instead.
memory/daily/*.md # Session logs
memory/knowledge/errors.md # Error patterns
memory/knowledge/learnings.md # Accumulated insights
Use Glob to find daily logs, then read the most recent ones.
Step 2: Analyze Patterns
Look for:
Recurring errors:
- Same error appearing 2+ times → needs a prevention rule
- Errors with "Status: Unresolved" → need attention
Workflow patterns:
- Tasks that repeat across sessions → candidate for automation (skill)
- Manual steps done every session → candidate for hook
- Decisions made repeatedly → candidate for rule
Time sinks:
- Topics that consumed lots of session time
- Context-switching patterns
- Blockers that persisted across sessions
Growth areas:
- Skills improving over time
- New tools or techniques adopted
- Knowledge gaps identified
Step 3: Generate Report
Output format:
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.
- 7d ago First seen · 124 lines · 104 tokens per session scan A 720bebffddb2
reflect is a skill published in the GitHub repository onurpolat05/ALBA (20 stars, last pushed 1mo ago), licensed MIT. It adds 104 tokens to every session and 910 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 2026-08-30.
Other skills, from other repositories
memory
Persistent, token-efficient project memory. When ON, maintains a .shob/memory/ folder of structured .md files so the full context of the project is NEVER lost across responses, sessions, or context compaction. Uses progressive disclosure — routes through a lightweight INDEX and loads only the files a task needs…
kn-extract
Use when extracting reusable patterns, decisions, failures, or knowledge into documentation.
kn-init
Use at the start of a new session to read project docs, understand context, and see current state.
save-learning
Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in…
atomic-wiki
Conversational wiki and capture-bucket routing. Fires when the user wants a place, space, or folder for notes, research, tickets, raw dumps, or knowledge capture — checks the block in /.claude/CLAUDE.md; if the cwd is under a registered realm, creates the folder as a bucket via atomic wiki bucket add rather than a…
catchup
Restore context after /clear by summarizing recent work and project state.