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 agentmods add skills/robinslange/learning-loop/reflectnpx skills add robinslange/learning-loop --skill reflectgit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00046 | $0.04329 |
| Opus 5 | $0.00023 | $0.02165 |
| Sonnet 5 | $0.00009 | $0.00866 |
| Haiku 4.5 | $0.00005 | $0.00433 |
Grade A, and why
reflect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect: Learning Consolidation
Overview
Structured checkpoint that extracts what was learned in this session and persists it to the right stores. This is how the learning loop closes: without this step, knowledge captured during the session rots.
When to Use
- End of a substantial work session (any domain: code, research, theorycrafting, coaching, anything)
- When the Stop hook nudges you
- When the user explicitly asks to consolidate or reflect
- After receiving multiple corrections in a session
Provenance
This skill emits provenance events for pipeline observability. Run each Bash command silently.
At session start:
node "${CLAUDE_PLUGIN_ROOT}/scripts/provenance-emit.js" '{"agent":"reflect","skill":"reflect","action":"session-start"}'
The session-end emit runs in Step 6, coalesced with the completion marker stamp (one final Bash block instead of two).
The PostToolUse hook handles both provenance emission and the per-write tracking that Step 4.6 (Upstream Refinement) consumes. Step 4 only needs to create the new-notes marker once; the hook appends every vault Write/Edit to it until Step 4.6.g removes the marker.
Process
Work through these steps in order. Be concise throughout: the vault voice is Hemingway, not Tolstoy.
Step 1: Session Review
Silently review the conversation. Identify:
- Domain: What area of work/knowledge was this? (project name, topic area)
- Nature: Was this building, debugging, researching, deciding, learning, discussing?
- Substance: Rate the session: was it routine or did genuine learning happen?
If the session was purely routine (config change, typo fix, quick lookup), say so and skip to Step 5. Not every session produces learnings.
Step 2: Extract Learnings
Identify what was learned. Categories:
| Category | Example | Destination | Confidence |
|---|---|---|---|
| Correction received | "Don't mock the DB in these tests" | Auto-memory (feedback) | strong |
| Preference revealed | "I prefer X approach over Y" | Auto-memory (user/feedback) | strong |
| Decision made | "We chose Postgres over SQLite because..." | Obsidian vault | - |
| Problem solved | "The build failed because X, fixed by Y" | Obsidian vault | - |
| Pattern discovered | "This pagination pattern works across projects" | Obsidian vault | - |
| Domain insight | "Resto Druid HoT uptime benchmarks are..." | Obsidian vault | - |
| Project context | "Auth rewrite is driven by compliance, not tech debt" | Auto-memory (project) | medium |
| Cross-project connection | "Same caching problem exists in Acme and Widget-Co" | Obsidian vault + links | - |
| Implicit pattern | User always runs tests before committing (observed 3+ times, never stated) | Auto-memory (feedback) | weak |
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.
- 2d ago First seen · 265 lines · 46 tokens per session scan A 0d8dfa68ff6d
reflect is a skill published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 10d ago), licensed Apache-2.0. It adds 46 tokens to every session and 4,329 once invoked, about $0.0002 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.
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