// [Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks.
How to drive the installed agent factory from a live coding-agent session. Covers when to use the factory versus direct edits, how to start the chain, where the human gates are, how to pick an implementer, how to interpret labels, and how to recover from the common stuck states. Use this skill when the user asks you to ship a feature, fix, or refactor through the factory; when they reference an issue or PR already moving through the chain; or when a workflow appears stuck and you need to explain the next operator action. Do NOT use this skill for scratch edits that will not go through the factory, read-only research, or tasks the user explicitly wants to bypass the factory.
Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.
[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.
Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
| name | pre-flight-check |
| description | [Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks. |
Surfaces relevant accumulated knowledge at the start of a session. This is the bridge that connects the outer loop back into the inner loop — it makes prior learnings visible before the agent starts work.
Without this, accumulated .learnings/ are invisible to new sessions. The agent repeats mistakes that were already captured because nobody told it to look.
The SessionStart hook (scripts/pre-flight.sh) does a fast scan and outputs a brief reminder if there are relevant signals:
<pre-flight-check>
Active learnings: N entries in .learnings/
Recent errors (last 7 days): N
Promotion-ready patterns: N
Failed evals: N
High-priority items:
- [Pattern-Key]: [one-line summary] (seen N times)
- [Pattern-Key]: [one-line summary] (seen N times)
Consider running /learning-aggregator if promotion-ready count > 0.
</pre-flight-check>
If there are no signals (empty .learnings/, no failed evals), the hook outputs nothing — zero overhead.
When invoked explicitly, the pre-flight check does a deeper analysis:
Read .learnings/LEARNINGS.md, .learnings/ERRORS.md, .learnings/FEATURE_REQUESTS.md.
For each entry, extract:
Read .evals/EVAL_INDEX.md for any failed or stale evals.
Look for unread files in .context-surfing/ (same as handoff-checker.sh but integrated).
If the user described the task area, filter learnings to:
Area matches the taskRelated Files overlap with likely-touched filesPriority: high/critical regardless of areaStatus: promotion_ready (need attention)## Pre-Flight Check
### Task Area: [inferred or stated]
### Relevant Learnings
| ID | Summary | Recurrence | Priority | Status |
|----|---------|-----------|----------|--------|
| LRN-... | ... | 3 | high | promotion_ready |
| ERR-... | ... | 2 | medium | pending |
### Key Warnings
- [Pattern-Key]: "Concise warning based on learning" — seen N times, last on YYYY-MM-DD
- [Pattern-Key]: "Concise warning based on learning" — seen N times, last on YYYY-MM-DD
### Failed Evals
| Eval ID | Pattern-Key | Last Failed | Recovery Action |
|---------|------------|-------------|-----------------|
| eval-... | ... | YYYY-MM-DD | ... |
### Handoff Files
- [filename] — from session on YYYY-MM-DD
### Recommendations
- [ ] Read handoff files before starting
- [ ] Run learning-aggregator (N promotion-ready patterns)
- [ ] Fix failed evals before starting new work
- [ ] Watch for [specific pattern] in [area]
.learnings/*.md — accumulated learning entries from self-improvement.evals/EVAL_INDEX.md — eval results from eval-creator.context-surfing/ — handoff files from context-surfingThis is where the blog's compounding happens:
Outer loop improves harness → pre-flight surfaces improvements → inner loop starts stronger
Every learning captured, every rule promoted, every eval created becomes visible at the next session start. The knowledge gaps get smaller with every cycle.
The hook script can be extended to use a local cache file (.pre-flight-cache.json) storing last-known state — entry counts, scan date, high-priority items — so the next session start only re-scans entries newer than the cached state. This would enable delta reporting ("since your last session, 2 new errors were logged and 1 pattern crossed the promotion threshold") and keep the hook near-instant regardless of how large .learnings/ grows. Not implemented today — the current hook scans directly on every session start.
.learnings/ files (read-only)