Agent-Skills.md

Agent Skills: 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.

UncategorizedID: pskoett/pskoett-ai-skills/pre-flight-check

Repository

pskoett/pskoett-ai-skills
pskoett
11422

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pnpm dlx add-skill https://github.com/pskoett/pskoett-ai-skills/tree/HEAD/plugin/skills/pre-flight-check

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plugin/skills/pre-flight-check/SKILL.md

Skill Metadata

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."

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.

For each entry, extract:

  • Pattern-Key, Summary, Priority, Status, Area, Related Files, Recurrence-Count, Last-Seen

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 Area matches the task
  • Entries whose Related Files overlap with likely-touched files
  • Entries with Priority: high/critical regardless of area
  • Entries with Status: promotion_ready (need attention)

Step 5: Output

## 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]

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 — Extension Point

Cache-based incremental scanning and repo-memory support are future enhancements. The current hook script scans .learnings/ directly on every session start.

The hook script can use a local cache file (.pre-flight-cache.json) to store last-known state: entry counts, scan date, high-priority items. On the next session start, it only re-scans entries newer than the cached state.

This enables delta reporting: "Since your last session, 2 new errors were logged and 1 pattern crossed the promotion threshold." More actionable than static counts, and near-instant regardless of how large .learnings/ grows.

When repo-memory is configured (see self-improvement), pre-flight-check reads from the memory branch — making prior learnings available even in fresh Codespaces or ephemeral environments.

What This Skill Does NOT Do

  • Does not modify .learnings/ files (read-only)
  • Does not promote patterns (that's harness-updater)
  • Does not run evals (that's eval-creator)
  • Does not block execution — it surfaces information, the agent decides what to act on
Pre-Flight Check Skill | Agent Skills