Github Copilot Sessions

GitHub Copilot Sessions

Locate past GitHub Copilot CLI sessions matching a project and/or topic, then summarize them. Session event logs reach tens of MB. Never read a full events.jsonl into the main context — rank cheaply with the bundled script, then dispatch one subagent per top candidate to grep just the relevant lines.

Storage facts

Two complementary stores live under ~/.copilot/:

1. ~/.copilot/session-store.db (SQLite WAL) — the fast BM25 path.

• search_index is an FTS5 virtual table; its built-in rank column is BM25 (lower = better).
• ~15 k indexed rows. source_type ∈ {turn, checkpoint_overview, checkpoint_history, checkpoint_work_done, checkpoint_technical_details, checkpoint_files, checkpoint_next_steps, workspace_artifact}.
• sessions(id, cwd, repository, branch, summary, created_at, updated_at, host_type).
• turns(session_id, turn_index, user_message, assistant_response, timestamp).

2. ~/.copilot/session-state//events.jsonl — per-session raw event log (one JSON obj/line).

• Each dir also has workspace.yaml with summary:, created_at:, cwd:, repository:.
• High-value event types: session.task_complete → data.summary (markdown recap); session.compaction_complete → data.summaryContent (~9 KB XML); user.message, assistant.message, tool.execution_complete.
• Session id = the UUID directory name.
• Used as BM25 fallback (--no-db) and for the deep-read step.

Procedure

1. Rank candidates cheaply — run the bundled script. Never grep raw event logs yourself first.

python3 /scripts/search_copilot_sessions.py [--project PATH] [--days N] [--limit N] [--no-db] KEYWORD...
# typical installed path:
python3 ~/.claude/skills/github-copilot-sessions/scripts/search_copilot_sessions.py "docker compose" "deployment"
# with project filter:
python3 ~/.claude/skills/github-copilot-sessions/scripts/search_copilot_sessions.py --project ~/workspace/OpenClawBot "provisioning"
# fallback to pure-Python BM25 over jsonl:
python3 ~/.claude/skills/github-copilot-sessions/scripts/search_copilot_sessions.py --no-db "provisioning"

• Default path uses FTS5 (fast, already indexed). Falls through to jsonl BM25 if DB is absent or returns nothing.
• Pass --no-db to force the Okapi BM25 path over events.jsonl files (stdlib, k1=1.5, b=0.75).
• Pass multiple keyword variants to increase recall ("merge gate" "PR gate" enforce testing).
• Output is JSON: {"query", "project", "source":"fts5"|"jsonl-bm25", "candidates":[…], "count"}.
• No-results case: JSON with empty candidates and a looked_for note.
• Browse mode (no keywords): returns most-recent N sessions — use when the user is vague ("what was I doing in X lately").

2. Deep-read the top 2–4 candidates with subagents — in parallel. Send all Task calls in a single message so they run concurrently. Each subagent must:

• Know the exact session_id and its events.jsonl path: ~/.copilot/session-state//events.jsonl.
• Grep within the file for the topic keywords; read only the surrounding lines. Forbid reading the whole file.
• Extract the nearby user.message content and any session.task_complete.data.summary text.
• Return a compact report: MATCH yes/no + confidence, 3–6 sentence summary of what was done, key files/commands/PRs, session date.

Subagent prompt: "Read the Copilot session at ~/.copilot/session-state/4bb4cd86-ab8b-4d6e-9f60-d5d4d16f7237/events.jsonl (JSONL, possibly tens of MB — do NOT read it whole). The user is looking for the session where they worked on VM provisioning timeouts. Use grep -n -i for 'provisioning', 'timeout', 'hetzner', 'tenant' to find relevant lines, then read only those line ranges (use sed -n 'START,ENDp'). Focus on user.message content and any session.task_complete summary. Report: MATCH yes/no + confidence; 3–6 sentence summary of what was done; key files/PRs/commands; session date. Under 200 words."

User: "find the Copilot session where I fixed the AKS managed identity issue"

1. Run the script:

``bash python3 ~/.claude/skills/github-copilot-sessions/scripts/search_copilot_sessions.py "AKS managed identity" "azure" --limit 6 ``

1. Script returns top 3 candidates via FTS5. Spawn 3 parallel subagents, one per candidate, each grepping for 'managed.identity', 'AKS', 'azure', 'workload.identity'.
2. Subagent for session 5c73286d reports MATCH high — found session.task_complete summary: "Fixed AKS workload identity binding by patching the federated credential in Terraform."
3. Present: session 5c73286d, repo VibeTechnologies/OpenClawBot, date 2026-04-18, summary above. Runner-ups: session a3f1... (weak match, only 2 keyword hits in turns).

User: "what was I doing in ~/workspace/paperclip last week?"

Browse mode — no specific keywords:

python3 ~/.claude/skills/github-copilot-sessions/scripts/search_copilot_sessions.py --project ~/workspace/paperclip --days 7

Returns the 8 most recent sessions for that project. Present titles + dates; ask the user which one to dive into.

3. Present the best match. Lead with the winning session: session_id, cwd/repository, date, and a summary of what was done. List runner-up candidates as one line each (id, date, why weaker). If nothing matches, show the most recent sessions for the project so the user can redirect.

Notes

• session.task_complete.data.summary is the highest-value ranking signal — it is a markdown recap of the full session. Prioritize it in subagent reads.
• GitHub Copilot CLI has no --resume equivalent; tell the user the session id and cwd so they can reconstruct context manually.
• Browse mode (no keywords): run the script without KEYWORD args, optionally with --days N.
• The jsonl BM25 path also covers sessions where assistant.message content is encrypted (blank) — it still scores on user messages, tool outputs, and task summaries.

Done when

The user has the matching session's session_id, cwd/repository, date, and a clear summary of what was accomplished — produced via targeted subagent greps, with the main context never having ingested a full transcript.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dzianisv
• Source: dzianisv/skills
• License: MIT

Install and usage instructions live in the source repository linked above.