Skill: AI session summary (context → summary)

Condenses a session context — the prompts and responses of one AI session, or the report produced by ai-session-report — into a short markdown summary on local disk. This skill covers only that transformation. Destination, credentials, and the policy for when to summarize are supplied by the caller (a knowledge note, a playbook, or the user).

session_summary.py beside this file assembles the file: it writes the front matter, title, and PR table, and takes the narrative body from you. Python 3 stdlib only.

1. Summary format

One summary per session, at $AI_SESSION_SUMMARY_DIR/<YYYY-MM-DD>-<id>.md (default directory ~/ai-session-summaries; <id> is the session id without any devin- prefix, derived from an id or a session URL).

---
date: 2026-09-15
session: devin-<id>
user: <username>
url: <session URL>
repo: <owner/repo worked on>
prompt: <one line: the initial request>
outcome: success | partial | failed
---

# Session summary <id>

## Pull requests

| Repository | PR | Title | State |
|---|---|---|---|
| nasa/fprime | [#5980](https://github.com/nasa/fprime/pull/5980) | Add ai-session-report skill | open |

## Request
## Result
## Notable events
## Lessons

The PR table lists every pull request produced or updated by the session (not PRs merely mentioned). With none, the table carries a single "No pull requests produced" row. The four body sections are written by you, each a few sentences or bullets:

• Request — what was asked, including later course corrections.
• Result — what was delivered: PRs, commits, files, notes, artifacts.
• Notable events — blockers, mistakes, corrections, surprises.
• Lessons — anything worth remembering for future sessions; omit the section rather than pad it.

2. Writing the summary

Gather the context first — the session's ai-session-report file if one exists, otherwise the event history (for Devin: devin_mcp → devin_session_events, as described in ai-session-report §3) — and the URL, title, and state of every PR the session produced. Then:

S=.github/skills/ai-session-summary/session_summary.py

python3 $S write --context "$SESSION_ID" --author "$USERNAME" \
  --from-report ~/ai-session-reports/2026-09-15-<id>.md \
  --meta url="$SESSION_URL" --meta repo="$OWNER_REPO" \
  --prompt "Add ai-session-report skill to F Prime" --outcome success \
  --pr "https://github.com/nasa/fprime/pull/5980 | Add ai-session-report skill | open" <<'EOF'
## Request
...

## Result
...

## Notable events
...

## Lessons
...
EOF

python3 $S path --context "$SESSION_ID"    # where the summary is

--from-report copies date, user, and any metadata from the report's front matter so they need not be repeated; explicit flags override it. Repeat --pr once per pull request; title and state are optional. write replaces the whole file, so re-run it to refresh a summary as the session progresses.

3. Rules

• Factual and condensed. Do not reproduce the exchanges; the report (or the session itself) is the record.
• Never write secret values into a summary.
• Report failures and reversals honestly — a summary that only lists successes is a defective summary.
• Summarizing is best-effort: a failure to write must not block the session's actual task.