focus-group
Synthetic audience pretesting (Justin Brooke "predictive wear" method). Run ANY outbound content — ads, emails, sales pages, newsletters, social posts, offers…
Reasoning recorder — fires automatically after cracking a HARD problem (multiple failed attempts, non-obvious insight, judgment call between viable options, or an invented quality bar). Captures HOW the problem was solved — problem shape, dead ends, the turning point, the
$ npx -y skills add josherau/claude-operating-core --skill extract-approach --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/extract-approachContext preview
The summary Claude sees to decide when to auto-load this skill.
Reasoning recorder — fires automatically after cracking a HARD problem (multiple failed attempts, non-obvious insight, judgment call between viable options, or an invented quality bar). Captures HOW the problem was solved — problem shape, dead ends, the turning point, the
name: extract-approach description: Reasoning recorder — fires automatically after cracking a HARD problem (multiple failed attempts, non-obvious insight, judgment call between viable options, or an invented quality bar). Captures HOW the problem was solved — problem shape, dead ends, the turning point, the transferable recipe — as one note in your learnings directory, so the reasoning survives the session and any model change. Do NOT invoke for routine work, mechanical fixes, or first-try solutions. Also use when the user says "extract that approach" or "write down how you did that."
Every hard solve contains two products: the fix, and the *approach that found the fix*. The fix ships; the approach evaporates when the session ends. This skill saves the approach — the more valuable half — as a permanent note any future model reads before touching similar work.
**Why this exists:** frontier models get repriced and retired. An answer helps once; a written-down approach upgrades every answer after it, at any model tier. The recipe requires top-tier judgment to *author* but only ordinary intelligence to *follow*.
Pick a learnings directory in your knowledge base (e.g. `{KNOWLEDGE_BASE}/learnings/`) and keep a `## Learnings` index — one line per lesson — in a file your sessions always load (CLAUDE.md is the natural place). Everything below assumes those two locations exist; adjust paths to your setup.
1. **It was actually hard.** At least one of: multiple failed attempts before the solve; a non-obvious insight was the turning point; a judgment call between 2+ viable approaches; a quality bar or heuristic was invented on the spot. 2. **It will recur.** The problem *shape* (not the exact problem) will plausibly show up again in the user's work. 3. **The approach is transferable.** You can state a recipe someone else could follow without this session's context.
If any fail, don't write a note — noise kills the learnings system faster than gaps do. Routine bug fixes, config edits, and lookups never qualify.
**Timing:** capture immediately at solve time, while the dead ends are still in context — never batched to session end. Logging mistakes the moment they happen is the same discipline; batching loses the details that matter.
Write ONE file to `{KNOWLEDGE_BASE}/learnings/{kebab-case-slug}.md`. The format is: one-line summary, then **Problem shape**, **Why it mattered**, **How to apply**:
{One-line summary: the transferable heuristic, stated so it stands alone in an index.}
**Problem shape:** {What class of problem this was — described abstractly enough that a future session pattern-matches it BEFORE knowing the solution. 1-2 sentences.}
**Why it mattered:** {What was at stake, what failed first and WHY each dead end failed — the dead ends are half the value; they prune a future model's search tree. Include the turning-point insight and what made it non-obvious.}
**How to apply:** {The recipe. Numbered steps a mid-tier model could follow cold: what to check first, what signal discriminates between causes, what order to try things, what to never do. If a quality bar was invented, state it as pass/fail criteria.}Rules:
Add one line to the `## Learnings` index in the file your sessions always load, matching the existing style:
- [{slug}](learnings/{slug}.md) — {one-line summary, compressed}The index is what every future session actually loads; an unindexed note is invisible.
Before writing, grep the learnings directory for the topic. If an existing lesson covers the same problem shape, **update that file** (sharpen the recipe, add the new dead end) instead of creating a near-duplicate. Delete lessons this solve proved wrong, and remove their index lines.
Quality gates for Claude Code. An agent should never grade its own work, outbound content should be pretested before it costs you money, research should be grounded in more than one perspective, and the reasoning behind a hard solve should outlive the session
Synthetic audience pretesting (Justin Brooke "predictive wear" method). Run ANY outbound content — ads, emails, sales pages, newsletters, social posts, offers…
Use when any substantive work product has been generated — copy, emails, documents, proposals, plans, code, designs, skills, newsletters — and is about to be…
Expert sales council with 7 advisors (Cialdini, Hormozi, Rackham, Voss, Klaff, Schwartz, Currier) for pricing, persuasion, negotiation, copywriting, pitch…
Set up and operate a file-based second brain — a knowledge vault that every Claude Code session reads on start and writes back to before it ends, so context…
Use when researching any topic, question, market, company, technology, or decision — before writing reports or proposals, making business or investment…
Recurring maintenance pass for a second-brain vault — link hygiene, naming, metadata/tags, Inbox filing, dedupe, learnings-index sync, index-note freshness,…