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 mp-web3/claude-starter-kit --skill reflectgit clone --depth 1 https://github.com/mp-web3/claude-starter-kitWrote 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/mp-web3/claude-starter-kit/reflect)<a href="https://agentmods.dev/skills/mp-web3/claude-starter-kit/reflect"><img src="https://agentmods.dev/badge/skills/mp-web3/claude-starter-kit/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/mp-web3/claude-starter-kit/reflect"><img src="https://agentmods.dev/badge/skills/mp-web3/claude-starter-kit/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 | $0.00046 | $0.01878 |
| Opus 5.5 | $0.00018 | $0.00751 |
| Sonnet 5.5 | $0.00009 | $0.00376 |
| Haiku 4.5 | $0.00005 | $0.00188 |
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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/reflect — Session Learning System
First: Read LEARNINGS.md (in this skill's directory) before proceeding.
You are running a 4-phase reflection workflow. Follow each phase in order.
Arguments: $ARGUMENTS
Phase 1: Extract
Run the extraction script to get candidate learnings from session JSONL.
python3 ~/claude-assistant/scripts/extract-learnings.py $ARGUMENTS
If $ARGUMENTS is empty, it analyzes the latest session. Pass --since YYYY-MM-DD or --file <path> to customize scope.
- If no pairs found -- tell user "Nothing to reflect on" and stop
- If pairs found -- capture the output and proceed to Phase 2
Phase 2: Analyze
For each candidate pair from Phase 1, determine:
-
Is this a real learning? ~40% are normal conversation — skip those. Look for:
- Corrections ("no, do X instead", "actually...", "wrong")
- Preferences ("always use...", "I prefer...", "from now on...")
- Tool rejections with feedback (user denied + said why)
- Workflow patterns (user repeatedly does something a specific way)
- Implicit feedback (user rephrases, asks again, provides what Claude should have known)
-
Check rejections — Read
~/claude-assistant/knowledge/self/rejections.md. For each candidate:- If it matches a previously rejected learning (same topic + target file): skip silently. Note in output: "Skipped: matches rejected learning from [date]"
- If it contradicts a previously rejected learning (opposite of what was rejected): flag as potential reversal — present to user with context from the rejection log
-
Classify and route each real learning using the routing table below.
-
Check for duplicates — Read the target file and verify the learning isn't already captured.
-
Scan for contradictions — For each proposed change with a target file: a. Read the target file b. Read up to 5 related files: same directory + shared tags (YAML
tagsfield) + wiki-linked files c. Scan for statements that directly contradict the proposed change d. If contradiction found, flag it for Phase 3 conflict resolution:CONFLICT with [file.md:line]: Existing: "[quoted text]" Proposed: "[new learning]"e. Update feedback counters — If the contradiction traces to a specific existing rule/learning entry, increment its
harmfulcounter in~/claude-assistant/state/rule-feedback.json
What ships with it
1 file 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 · 185 lines · 46 tokens per session scan A 16f865ef2b59
reflect is a skill published in the GitHub repository mp-web3/claude-starter-kit (109 stars, last pushed 7mo ago), licensed MIT. It adds 46 tokens to every session and 1,878 once invoked, about $0.0002 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 2026-08-30.
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