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 NikiforovAll/claude-code-rules --skill reflectgit clone --depth 1 https://github.com/NikiforovAll/claude-code-rulesWrote 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/nikiforovall/claude-code-rules/reflect)<a href="https://agentmods.dev/skills/nikiforovall/claude-code-rules/reflect"><img src="https://agentmods.dev/badge/skills/nikiforovall/claude-code-rules/reflect/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/nikiforovall/claude-code-rules/reflect"><img src="https://agentmods.dev/badge/skills/nikiforovall/claude-code-rules/reflect.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 | $0.00000 | $0.01750 |
| Opus 5.5 | $0.00000 | $0.00700 |
| Sonnet 5.5 | $0.00000 | $0.00350 |
| Haiku 4.5 | $0.00000 | $0.00175 |
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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/reflect — session reflection
Produce a single-file interactive HTML dashboard analyzing a Claude Code session for places the agent took a wrong turn, paired with concrete repo additions that would prevent the same wrong turn next time.
When to use
- User invokes
/reflect(no args) → analyze the current session from the in-context conversation. Do not re-read the session JSONL — work from the agent's own memory of what happened. - User invokes
/reflect <session-id>or/reflect <path-to-jsonl>→ analyze a different session. Runscripts/analyze_session.py <path>directly (this skill runs in a forked context —context: fork— so the compressed transcript can enter your context safely). The script outputs a compact markdown transcript: system reminders stripped, tool calls/results collapsed to one-liners, compaction blocks expanded with embedded user quotes. Then you analyze that transcript to identify wrong-turn moments — the script does NOT classify incidents, it only compresses.
Session JSONLs live under: ~/.claude/projects/<encoded-cwd>/<session-id>.jsonl
What to detect (incidents)
Open-ended — but the common shapes are:
- correction — user pushed back on an approach (
"no","don't","stop","actually") - retry — agent ran the same tool 2+ times with variations before it worked
- waste — many Reads/Greps/Globs before finding the right file
- reversal — agent edited then unwound (Edit → revert, Write → delete)
- dead-end — tool failed because of the environment (missing binary, wrong path, OS mismatch)
- self-correction — agent caught its own mistake mid-stream
Severity:
- high — explicit user correction, repeated correction, or substantial wasted turns
- med — single retry/reversal, modest waste
- low — minor dead-end, easily recovered
Confidence (0–100): how sure you are this is a real wrong turn worth surfacing, vs. signal noise. Be honest — low-confidence items are not bugs, they let the user filter out speculation.
- 90–100 — explicit user correction or unambiguous failure
- 70–89 — strong pattern (repeated retries, clear reversal) with minor interpretation
- 50–69 — plausible wrong turn, could also be normal exploration
- <50 — speculative; surface only if the pattern is interesting
What ships with it
2 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 · 107 lines · 0 tokens per session scan A 78dc68203be7
reflect is a skill published in the GitHub repository NikiforovAll/claude-code-rules (142 stars, last pushed 23d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,750 tokens. 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 2026-09-21.
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