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.
npx skills add rodrigo-kiko/claude-reflect-skill --skill skillgit clone --depth 1 https://github.com/rodrigo-kiko/claude-reflect-skillWrote 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/rodrigo-kiko/claude-reflect-skill/skill)<a href="https://agentmods.dev/skills/rodrigo-kiko/claude-reflect-skill/skill"><img src="https://agentmods.dev/badge/skills/rodrigo-kiko/claude-reflect-skill/skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rodrigo-kiko/claude-reflect-skill/skill"><img src="https://agentmods.dev/badge/skills/rodrigo-kiko/claude-reflect-skill/skill.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.00994 |
| Opus 5.5 | $0.00027 | $0.00398 |
| Sonnet 5.5 | $0.00014 | $0.00199 |
| Haiku 4.5 | $0.00007 | $0.00099 |
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 1mo 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect Skill - Session Analysis & Learning Extraction
This skill analyzes development conversations to extract actionable learnings that improve future coding sessions by updating the project's CLAUDE.md file.
Language Adaptation
Detect and match the user's working language for all outputs:
- Session analysis reports
- Learning descriptions
- Proposed changes
- Confirmation prompts
Keep technical terms (file paths, function names, code snippets) in their original form.
Core Workflow
Step 1: Scan Entire Conversation
Analyze the COMPLETE current conversation looking for signals. See references/signal-patterns.md for detailed patterns.
Primary signals to detect:
-
Explicit Corrections → HIGH confidence
- User says "No, don't do X" / "Use Y instead"
- User states "Never/Always do X"
- User explains why something was wrong
-
Successful Patterns → MEDIUM confidence
- Approaches that worked without objection
- Code patterns user accepted
- Structures repeated successfully
-
Revealed Preferences → LOW confidence
- Style choices (naming, comments, structure)
- Communication preferences
- Single observations needing validation
Step 2: Classify by Confidence Level
Apply criteria from references/confidence-levels.md:
- HIGH: Direct, unambiguous corrections or rules from user
- MEDIUM: Patterns that worked well, implicit preferences confirmed by acceptance
- LOW: Single observations that need future validation
Step 3: Generate Analysis Report
Format output per references/output-format.md:
- Summary header with signal count
- Learnings grouped by confidence level
- Source reference for each learning
- Diff preview of proposed CLAUDE.md changes
- Action prompt for user approval
Step 4: Apply Changes (ONLY with explicit approval)
CRITICAL RULES:
- NEVER modify any file without explicit user approval
- Show EXACT diff of what will be added
- Wait for user to type Y, N, or E
- Preserve ALL existing content in CLAUDE.md
What ships with it
3 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.
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.
- 1mo ago First seen · 139 lines · 68 tokens per session scan A 2124f67fe52c
reflect is a skill published in the GitHub repository rodrigo-kiko/claude-reflect-skill (2 stars, last pushed 9mo ago), licensed MIT. It adds 68 tokens to every session and 994 once invoked, about $0.0003 per session on Opus 5.5. 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.
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