context-usage

name	context-usage
description	Analyze current session context and token usage from OpenCode SQLite database
platforms	["opencode"]
triggers	["context usage","token usage","session stats","how much context","context budget","how big is this session","session size"]

Context Usage Analysis

Recommended model tier: balanced (sonnet) - straightforward SQL queries

Analyze the current session's context window consumption, tool usage breakdown, and token costs by querying the OpenCode SQLite database directly.

Prerequisites

• This skill only works on OpenCode. Verify by checking the environment:
  • $OPENCODE=1 — set by the OpenCode runtime
  • $AIDE_PLATFORM=opencode — set by aide when running under OpenCode
  • $AIDE_SESSION_ID — the current session ID (injected by aide)
• The OpenCode database is at ~/.local/share/opencode/opencode.db.
• sqlite3 must be available on the system.

If $OPENCODE is not 1 or $AIDE_PLATFORM is not opencode, abort immediately and inform the user that this skill is only supported on OpenCode. Do not attempt to query other databases (e.g. Claude Code's storage) — the schema is OpenCode-specific.

If $AIDE_SESSION_ID is not set, abort with a message explaining that the session ID could not be determined.

Workflow

Run the following queries sequentially in a single Bash call (chain with &&). Present results to the user in a formatted summary after all queries complete.

Step 1: Validate environment

test "$OPENCODE" = "1" && echo "Platform: OpenCode" || echo "ERROR: Not running on OpenCode (OPENCODE=$OPENCODE)"
test "$AIDE_PLATFORM" = "opencode" && echo "AIDE Platform: opencode" || echo "WARNING: AIDE_PLATFORM=$AIDE_PLATFORM"
test -n "$AIDE_SESSION_ID" && echo "Session: $AIDE_SESSION_ID" || echo "ERROR: AIDE_SESSION_ID not set"

If OPENCODE is not 1, stop immediately — this skill cannot work outside OpenCode. If AIDE_SESSION_ID is not set, stop and inform the user.

Step 2: Session overview

sqlite3 ~/.local/share/opencode/opencode.db "
SELECT
  s.title,
  s.slug,
  ROUND((julianday('now') - julianday(datetime(s.time_created/1000, 'unixepoch'))) * 24, 1) as hours_old,
  (SELECT COUNT(*) FROM message m WHERE m.session_id = s.id) as messages,
  CASE WHEN s.time_compacting IS NOT NULL THEN 'yes' ELSE 'no' END as compacted
FROM session s
WHERE s.id = '$AIDE_SESSION_ID';
"

Step 3: Token totals

Sum tokens from step-finish parts (each represents one LLM turn):

sqlite3 ~/.local/share/opencode/opencode.db "
SELECT
  SUM(json_extract(data, '$.tokens.input')) as input_tokens,
  SUM(json_extract(data, '$.tokens.output')) as output_tokens,
  SUM(json_extract(data, '$.tokens.cache.read')) as cache_read_tokens,
  SUM(json_extract(data, '$.tokens.cache.write')) as cache_write_tokens,
  SUM(json_extract(data, '$.tokens.total')) as total_tokens,
  COUNT(*) as llm_turns
FROM part
WHERE session_id = '$AIDE_SESSION_ID'
  AND json_extract(data, '$.type') = 'step-finish';
"

Step 4: Tool output breakdown

Show tool usage ranked by total output size:

sqlite3 ~/.local/share/opencode/opencode.db "
SELECT
  json_extract(data, '$.tool') as tool,
  COUNT(*) as calls,
  SUM(length(json_extract(data, '$.state.output'))) as total_output_bytes,
  ROUND(AVG(length(json_extract(data, '$.state.output')))) as avg_bytes,
  MAX(length(json_extract(data, '$.state.output'))) as max_bytes
FROM part
WHERE session_id = '$AIDE_SESSION_ID'
  AND json_extract(data, '$.type') = 'tool'
GROUP BY tool
ORDER BY total_output_bytes DESC;
"

Step 5: Total session size

sqlite3 ~/.local/share/opencode/opencode.db "
SELECT
  SUM(length(json_extract(data, '$.state.output'))) as tool_output_bytes,
  SUM(length(json_extract(data, '$.state.input'))) as tool_input_bytes,
  SUM(length(data)) as total_part_bytes
FROM part
WHERE session_id = '$AIDE_SESSION_ID'
  AND json_extract(data, '$.type') = 'tool';
"

Output Format

Present the results as a structured summary:

## Session Context Usage

**Session:** <title> (<slug>)
**Age:** <hours> hours | **Messages:** <count> | **Compacted:** yes/no

### Token Usage
| Metric | Count |
|--------|-------|
| Input tokens | <n> |
| Output tokens | <n> |
| Cache read | <n> |
| Cache write | <n> |
| **Total tokens** | **<n>** |
| LLM turns | <n> |

### Tool Output Breakdown (by total bytes)
| Tool | Calls | Total Output | Avg/call | Max |
|------|-------|-------------|----------|-----|
| ... | ... | ... | ... | ... |

### Session Size
- Tool outputs: <n> KB
- Tool inputs: <n> KB
- Total part storage: <n> KB (includes JSON metadata overhead)

Format byte values as KB (divide by 1024, round to 1 decimal). Highlight the top 3 tools by total output as the biggest context consumers. If any single tool call exceeds 20KB, flag it as a potential optimization target.