chronicle

name	chronicle
description	Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting session data.

Chronicle

Analyze the user's Copilot session history using the copilot_sessionStoreSql tool. This skill handles standup reports, usage analysis, session search, and session store maintenance.

Sessions may be stored locally (SQLite) and optionally synced to the cloud for cross-device access. Cloud sync is controlled by the chat.sessionSync.enabled setting.

Prerequisite: Chronicle requires the github.copilot.chat.localIndex.enabled setting to be true. If the copilot_sessionStoreSql tool is not available, tell the user to enable this setting in VS Code Settings.

Available Tool Actions

The copilot_sessionStoreSql tool supports three actions:

Action	Purpose	query param
standup	Pre-fetch last 24h sessions, turns, files, refs	Not needed
query	Execute a read-only SQL query	Required
reindex	Rebuild local session index + cloud sync	Not needed

Workflows

Standup

When the user asks for a standup, daily summary, or "what did I do":

1. Call copilot_sessionStoreSql with action: "standup" and description: "Generate standup".
2. The tool returns pre-fetched session data (sessions, turns, files, refs from the last 24 hours).
3. If the result is empty, tell the user no sessions were found in the last 24h, suggest /chronicle:reindex, and stop — do not fabricate a standup.
4. For any PR references in the data, check their current status (open, merged, draft) if possible.
5. Format the returned data as a standup report grouped by work stream (branch/feature):

Standup for <date>:

**✅ Done**

**Feature name** (`branch-name` branch, `repo-name`)
  - 3-7 words describing the status
  - Key files: 2-3 most important files changed
  - Merged: [#123](link)
  - Session: `session-id`

**🚧 In Progress**

**Feature name** (`branch-name` branch, `repo-name`)
  - 3-7 words describing the current state of work
  - Key files: 2-3 most important files being worked on
  - Draft: [#789](link)
  - Session: `session-id`

Rules:

• Keep it concise and succinct — the user can always ask follow-up questions
• Use turn data (user messages AND assistant responses) to understand WHAT was done
• Use file paths to identify which components/areas were affected
• Group related sessions on the same branch into one entry
• For sessions, only show the most recent session per feature/branch
• Link PRs and issues using markdown link syntax
• Classify as Done if work appears complete, In Progress otherwise

Tips

When the user asks for tips, workflow recommendations, or how to improve:

Step 1: Investigate how the user works

Use copilot_sessionStoreSql with action: "query" to explore their recent sessions. The goal is to understand their patterns — how they prompt, what tools they use, and where they spend time.

Queries to run (do not explain what you will do first — start querying immediately):

• Sessions from the last 7 days: counts, durations, repositories
• Turn data: read actual user messages to understand prompting patterns
• session_files: which files and tools are used most frequently
• session_refs: PR/issue/commit activity patterns

Step 2: Consider available features

If the current workspace has a .github/ folder, check for .github/copilot-instructions.md, .github/skills/, and .github/agents/ to see what custom configuration exists. Do NOT look outside the workspace. Look for gaps between what's available and what the user actually uses.

Step 3: Provide tips

Based on what you learned, provide 3-5 specific, actionable tips. Each tip should:

• Be grounded in actual usage data — reference specific patterns you observed
• Be non-obvious — skip basic features that any regular user would already know
• Focus on gaps where a feature, workflow change, or different approach would meaningfully improve their experience

Analysis dimensions to explore:

• Prompting patterns: Are user messages vague or specific? Do they provide context? Do they correct or redirect the agent frequently?
• Tool usage: Which tools are used most? Are there underutilized tools that could help?
• Session patterns: How long are sessions? Are there many short abandoned sessions?
• File patterns: Which areas of the codebase get the most attention? Any repeated edits to the same files?
• Workflow: Is the user leveraging agent mode, custom instructions, prompt files, skills?

If the session store has little data, acknowledge that and suggest features to try based on what configuration you found in the workspace.

Cost Tips

When the user asks for cost tips, ways to reduce token usage, or how to lower Copilot spend (e.g. /chronicle:cost-tips):

The goal is personalized, data-grounded recommendations for reducing token usage — not a generic checklist. Every tip must point to a specific pattern you observed in their data.

Cost-relevant schema (in addition to the Database Schema section below)

• Cloud DuckDB only — the local SQLite store does not record per-event token usage and has no events table. If the active backend is local, gate all token queries and tell the user that real token-level analysis requires enabling cloud sync (chat.sessionSync.enabled).
• events (cloud): per-event billing — rows where type = 'assistant.usage' carry usage_input_tokens, usage_output_tokens, usage_model. To break spend down by agent type, JOIN events e to sessions s ON s.id = e.session_id and group by s.agent_name.
• sessions.agent_name / agent_description (both backends): values like VS Code agent (VS Code chat), Copilot CLI, Copilot Coding Agent, Copilot Code Review, or custom agents/subagents. Use to break spend down by agent type.
• Use LENGTH(user_message) on turns (or LENGTH(user_content) on events where type = 'user.message') to find oversized pastes.

Step 1: Investigate cost and token patterns

Use copilot_sessionStoreSql with action: "query". What to investigate depends on the active backend.

Cloud (DuckDB) — start with agent-type awareness:

The session store mixes session types via sessions.agent_name (join events to sessions on session_id to get the agent for any per-event analysis). Your advice is only useful if you know which agents the user actually runs, so this is the first thing to learn.

• Enumerate every agent in use. Run e.g. SELECT agent_name, agent_description, COUNT(*) AS n FROM sessions WHERE updated_at > now() - INTERVAL '30 days' GROUP BY 1, 2 ORDER BY n DESC so you see the full inventory — official agents and any custom agents/subagents in agent_description. Do not assume.
• Decide which to advise on. Include any agent type the user can make cheaper: VS Code agent (VS Code chat — usually the dominant agent), Copilot CLI (interactive terminal), Copilot Coding Agent (autonomous cloud tasks), custom agents and subagents. Always exclude agent_name = 'Copilot Code Review' and any other agent the user does not drive interactively.
• Tailor advice per agent. VS Code agent tips (compaction, model picker, fresh chats, .github/copilot-instructions.md, custom skills/agents) look different from CLI tips (compaction, model switching, subagent delegation), Coding Agent tips (prompt scoping, smaller task framing), and custom-agent tips (slimming tool lists, narrowing prompts).

Then drill into cost patterns (filter events rows by type = 'assistant.usage' for billable rows):

• Token-heavy sessions and turns — sum usage_input_tokens and usage_output_tokens per session and per model from events where type = 'assistant.usage'. Which sessions burned the most tokens? Which models?
• Input-to-output ratios — when input tokens dwarf output tokens, the user is paying to re-send a bloated context every turn. Strongest signal that compaction, smaller working sets, or fresh sessions would help.
• Model mix — break down spend by usage_model. Are premium models being used for routine work (renames, simple edits, status checks) that a cheaper model could handle?
• Per-turn growth — within long sessions, does usage_input_tokens keep climbing turn-over-turn? Strong signal that compaction wasn't used.
• Oversized pastes — LENGTH(user_content) on events where type = 'user.message' to find user messages that should have been file references (also visible in session_files as repeated reads of the same path within one session).
• Group cost breakdowns by agent_name (and agent_description where useful) in at least one query so the user sees where their spend actually goes — and so you spot if a single custom agent dominates.

Local (SQLite) — no token data; use proxies:

• Long sessions without compaction — sessions with many turns and no rows in checkpoints (each checkpoints row is a successful compaction). LEFT JOIN checkpoints c ON c.session_id = s.id WHERE c.session_id IS NULL + a turn-count threshold gives prime candidates.
• Late compaction — for sessions that do have checkpoints, compare checkpoints.checkpoint_number and created_at against the session's turn count. A first compaction at turn 60 of an 80-turn session is far less helpful than one at turn 25.
• Repeated large file reads — in session_files, look for the same file read many times within one session, or across sessions.
• Tool-call thrash — sessions with many turns and repeated tool calls often indicate the agent rediscovered the same context multiple times.
• Oversized pastes — use LENGTH(user_message) on turns to find very long user messages that should have been file references.

Both backends:

• Long-running sessions — sessions with many turns or that span many hours drag a growing context window across every turn.
• Repeated work — the same file/topic showing up in many sessions, or the same agent stumbling block recurring (suggesting a custom skill, agent, or copilot-instructions.md entry would let the model do the work in one shot).
• Subagent usage — are heavyweight investigations being run in the main session (paying for their tokens to live in main context) when they could be delegated to a subagent that returns only a summary?

Drill into a few of the most expensive sessions and read the actual conversation turns to understand why they were expensive. Don't just report aggregates — explain the cause.

Step 2: Map findings to features and habits

If the current workspace has a .github/ folder, check .github/copilot-instructions.md, .github/skills/, and .github/agents/ to see what custom configuration already exists. Do NOT look outside the workspace. Cost-relevant capabilities to keep in mind:

• Mid-session compaction (e.g. /compact) to shrink the context window; for users who never compact, this is often the single biggest win.
• Model picker — switch to a cheaper model for routine work; check whether premium models are being used for simple tasks.
• Starting a fresh chat instead of continuing a bloated session.
• Subagents/delegation for offloading heavy research into a sub-context whose tokens don't accrete into the main session.
• Custom skills (.github/skills/) and custom agents (.github/agents/) so repeated workflows don't re-derive context each time.
• .github/copilot-instructions.md to encode project conventions the model otherwise has to be told every session.
• For cloud-enabled users, the Copilot usage view to inspect current premium-request spend.

Step 3: Provide tips

Give the user 3-5 specific, actionable tips. Each tip should:

• Be grounded in their data — reference a specific session, file, model, or pattern you observed (with rough numbers when you have them: turn counts, token totals, file-read counts, etc.).
• Be non-obvious — skip basics any returning user already knows. Assume they know compaction and fresh chats exist; help them notice they're not using them where it would matter.
• Quantify the win when possible — "compacting around turn 30 of that 80-turn session would have shaved ~X input tokens off every subsequent turn" is far better than "consider compacting".
• Be concrete — name the workflow change, command, or config file edit. If the suggestion is a custom skill or agent, sketch what it would cover.
• Match the agent type — if a finding is specific to one agent_name, say so. Don't propose CLI-only fixes for findings from Coding Agent sessions, and vice versa.

If the session store has little data (e.g., cloud store is empty, or only a handful of local sessions), say so plainly and offer 2-3 non-obvious cost-saving habits anchored in available features rather than fabricating findings. If the user is on local-only storage, end by noting that enabling chat.sessionSync.enabled unlocks per-event token analysis for sharper future tips.

Search

When the user asks to search, find, or look up past sessions by keyword (e.g. /chronicle:search <query>):

Search strategy

1. Search across session summaries, conversation turns (user messages AND assistant responses), and any other indexed content (checkpoints, file paths, refs like PR/issue/commit). The user's query may match a topic, a file path, or a PR/issue number — cover all three.
2. For each matching session, gather enough metadata to label it: s.id, s.repository, s.branch, s.summary, s.updated_at, plus a short snippet that shows why it matched (e.g. substr(user_message, 1, 160) on cloud, or the matched file_path / ref_value).
3. Present results grouped by repository, ordered by most recently updated.

Writing the query

Call copilot_sessionStoreSql with action: "query" and description: "Search sessions for <query>". Follow the SQL dialect shown in the tool description (SQLite vs DuckDB).

Schema essentials (full schema is in the Database Schema section below — re-read it before writing the query):

• The sessions table primary key is id (NOT session_id). Every other table uses session_id as the FK back to sessions.id. Always project s.id from sessions.
• Don't invent names: there is no started_at (use created_at/updated_at), no workspace (use cwd locally; cloud has none), no title (use summary), no content/messages (use turns.user_message/turns.assistant_response, or on cloud events.user_content/events.assistant_content).
• Local SQLite: prefer the FTS5 search_index table (WHERE search_index MATCH '<query>') for body content — search_index already has a session_id column, so select it directly (SELECT session_id, content FROM search_index WHERE search_index MATCH ...). Do not join search_index.rowid to turns.rowid; they are unrelated and you will pull in unrelated sessions. File paths and refs aren't in the FTS index — combine with LIKE on session_files.file_path and session_refs.ref_value.
• Cloud DuckDB: no FTS5 — use ILIKE '%<query>%' across the text columns of sessions, turns, checkpoints, session_files, session_refs.

Escape single quotes in the user's query by doubling them (it's → it''s). For multi-word FTS5 queries, quote the whole phrase: MATCH '"apply patch"'.

Performance — avoid cloud timeouts

ILIKE '%X%' on turns is a full table scan. Cloud queries that run too long return context deadline exceeded. To stay under the budget:

• Collect matching session IDs into a single CTE (WITH hits AS (...)) and then JOIN back to sessions once. Don't use correlated subqueries in the SELECT list — they re-scan the CTE per row.
• Aggregate match info per session with GROUP BY session_id and any_value() / MIN() / array_agg() rather than scalar subqueries.
• On cloud, default to a 90-day window on the heavy tables (WHERE timestamp >= now() - INTERVAL '90 days' on turns, same on checkpoints via created_at). Mention the window in your summary line so the user knows it's bounded; widen it if the user asks for "all time" or no results come back.
• Keep LIMIT 50 on the final SELECT.
• If a query times out, retry with a tighter window (30 days) or drop the heaviest table (usually turns) and tell the user what you trimmed.

Output format

For each session, build a one-line label from summary if present, else from the returned snippet (truncate to ~80 chars). Never emit (no summary), (no metadata), or bare session-id lists.

If you applied a time window (e.g. the cloud 90-day default), include it in the header so the user knows the scope; otherwise omit the scope phrase or say "all time". Example:

**Search results for "<query>"** (<n> sessions, <scope: e.g. "last 90 days" / "all time">)

_owner/repo_
- `session-id` — **<summary or snippet>**
  `branch` · updated <relative time> · matched in <match_kind>
- `session-id` — **<summary or snippet>**
  `branch` · updated <relative time> · matched in <match_kind>

_other-owner/other-repo_
- ...

Rules:

• Always render one session per line — never join multiple session ids with commas.
• The label is summary if non-null; otherwise the snippet; otherwise a usable fallback like the matched file path. If even those are empty, skip the session rather than show "no summary".
• Include · matched in <match_kind> (turn / file / ref / checkpoint / meta) when you can — helps the user see why each session matched.
• Group by repository. Sessions whose repository is NULL go under an Other heading, formatted the same way (one per line, with a usable label).
• Cap visible results per repository at ~10; if there are more, append …and N more (refine your query).

No results

If no rows are returned, tell the user and suggest:

• Trying different keywords or a broader search term (single word, or a substring instead of a phrase)
• Widening the time window ("search all time", "include older sessions")
• Running /chronicle:reindex if they haven't indexed their sessions yet
• Running /chronicle:standup to see recent activity

Reindex

When the user asks to reindex, rebuild, or refresh their session store:

1. Call copilot_sessionStoreSql with action: "reindex" and description: "Reindex sessions".
2. The tool rebuilds the local session store from debug logs and, if cloud sync is enabled, uploads new sessions to the cloud.
3. Present the before/after stats and cloud sync results to the user.

If the user says "force reindex" or wants to re-process already-indexed sessions, add force: true to the call. By default, already-indexed sessions are skipped for speed.

Delete Sessions

When the user asks to delete session data or clear their history:

• Guide them to run the Delete Session Sync Data command from the Command Palette (github.copilot.sessionSync.deleteSessions).
• This command lets them choose which sessions to delete from both local storage and the cloud.
• The tool itself does NOT support deletion — this is intentional to prevent accidental data loss.

Query Guidelines

When using action: "query":

• Only one query per call — do not combine multiple statements with semicolons
• Only SELECT and WITH (CTE) are allowed. DESCRIBE, SHOW, PRAGMA, and any mutating statements are blocked — re-read the Database Schema section below instead of trying to introspect
• Always use LIMIT (max 100) and prefer aggregations (COUNT, GROUP BY) over raw row dumps
• Query the turns table for conversation content — it gives the richest insight into what happened
• Query session_files for file paths and tool usage patterns
• Query session_refs for PR/issue/commit links
• Join tables using session_id for complete analysis
• Always filter on updated_at (not created_at) for time ranges
• Always JOIN sessions with turns to get session content — do not rely on sessions.summary alone

Query routing

The tool automatically routes queries based on the user's cloud sync settings:

• Cloud enabled: Queries go to the cloud DuckDB backend which contains ALL sessions across devices and agents (VS Code, CLI, Copilot Coding Agent, PR reviews). The tool description will show DuckDB SQL syntax — follow it.
• Cloud disabled: Queries go to local SQLite which only contains sessions from this device. The tool description will show SQLite syntax.

The tool's description dynamically changes based on the active backend. Always follow the SQL syntax shown in the tool description — it matches the active backend.

Database Schema

Tables (both local and cloud unless noted)

• sessions: id, cwd (workspace folder path — always NULL in cloud), repository, branch, host_type, summary, agent_name, agent_description, created_at, updated_at
• turns: session_id, turn_index, user_message, assistant_response (first ~1000 chars, may be truncated), timestamp
• checkpoints: session_id, checkpoint_number, title, overview, history, work_done, technical_details, important_files, next_steps, created_at — compaction checkpoints storing summarized state. Note: cloud has fewer columns (no history/work_done/technical_details).
• session_files: session_id, file_path, tool_name, turn_index, first_seen_at
• session_refs: session_id, ref_type (commit/pr/issue), ref_value, turn_index, created_at
• search_index: FTS5 virtual table (local only). Columns: content, session_id, source_type (turn/assistant/checkpoint/etc.), source_id. Use WHERE search_index MATCH 'query' for full-text search and project session_id directly — do NOT join search_index.rowid to turns.rowid (the rowids are independent and the join will match the wrong rows). Use snippet(search_index, 0, '[', ']', '…', 12) or substr(content, 1, 160) for a snippet.

Cloud-only tables

• events: Raw event table (~90 columns). Key columns: session_id, timestamp, type, user_content, assistant_content, tool_start_name, tool_complete_success, tool_complete_result_content, usage_model, usage_input_tokens, usage_output_tokens
• tool_requests: session_id, tool_call_id, name, arguments_json

Date math (SQLite): datetime('now', '-1 day'), datetime('now', '-7 days') Date math (Cloud/DuckDB): now() - INTERVAL '1 day', now() - INTERVAL '7 days'. Use ILIKE for text search (no FTS5/MATCH), date_diff('minute', start, end) for durations.