pre-flight-check
[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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/pskoett/pskoett-ai-skills --skill pre-flight-check- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pre-flight-check automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Pre-Flight Check
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.
When It Runs
- Automatically via SessionStart hook (lightweight scan, ~100-200 tokens)
- Manually before major tasks (deep scan with area filtering)
Hook Output (Automatic — Lightweight)
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.
Manual Deep Scan
When invoked explicitly, the pre-flight check does a deeper analysis:
Step 1: Scan .learnings/
Read .learnings/LEARNINGS.md, .learnings/ERRORS.md, .learnings/FEATURE_REQUESTS.md, and .learnings/HEALS.md (the last from self-healing — verified runtime fixes filed during prior sessions; surface these prominently so the agent applies known fixes before reinventing them).
For each entry, extract:
- Pattern-Key, Summary, Priority, Status, Area, Related Files, Recurrence-Count, Last-Seen
- For HEAL entries: also Active-Context, Trigger, and any Handoff block flagging promotion readiness
Step 2: Scan .evals/ (if exists)
Read .evals/EVAL_INDEX.md for any failed or stale evals.
Step 3: Check Context-Surfing Handoffs
Look for unread files in .context-surfing/ (same as handoff-checker.sh but integrated).
Step 4: Relevance Filter
If the user described the task area, filter learnings to:
- Entries whose
Areamatches the task - Entries whose
Related Filesoverlap with likely-touched files - Entries with
Priority: high/criticalregardless of area - Entries with
Recurrence-Count>= 3 (promotion-ready by recurrence threshold — need attention)
Step 5: Output
## Pre-Flight Check
### Task Area: [inferred or stated]
### Relevant Learnings
| ID | Summary | Recurrence | Priority | Status |
|----|---------|-----------|----------|--------|
| LRN-... | ... | 3 | high | pending |
| 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]
Integration
Upstream (feeds from)
.learnings/*.md— accumulated learning entries from self-improvement.evals/EVAL_INDEX.md— eval results from eval-creator.context-surfing/— handoff files from context-surfing
Downstream (feeds into)
- Inner loop context — the agent starts work with awareness of known patterns
- learning-aggregator — if promotion-ready count is high, recommend running it
- eval-creator — if failed evals exist, recommend fixing before new work
The Compounding Effect
This 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.
Incremental Scanning (future enhancement)
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.
What This Skill Does NOT Do
- Does not modify
.learnings/files (read-only) - Does not promote patterns (that's the harness-updater plugin agent, or a human applying the gap report when the plugin isn't installed)
- Does not run evals (that's eval-creator)
- Does not block execution — it surfaces information, the agent decides what to act on
FAQ
What does the pre-flight-check skill do?
[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 do I install pre-flight-check?
Run npx skills add https://github.com/pskoett/pskoett-ai-skills --skill pre-flight-check in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pre-flight-check/ (personal) or .claude/skills/pre-flight-check/ (project).
Which AI agents can use pre-flight-check?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pre-flight-check safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains pre-flight-check?
It is published by pskoett in pskoett/pskoett-ai-skills. It has 69 recorded installs.
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