Briefing

Briefing Command

Triggers the Chief of Staff to generate an executive summary.

Usage

/briefing [period]
# Example: /briefing today
# Example: /briefing week
/briefing --tokens [--since 7d|24h|30m]
# Reports real session token usage from Claude Code JSONL.

Protocol

1. Collect: Gather logs from kb/learnings/, maintenance/ logs, and recent runs.
2. Synthesize: Group by category (Ops, Strategy, Actions).
3. Filter: Remove low-priority success logs.
4. Present: Render the Daily Brief.

Example

## Daily Brief — 2026-04-23

### Ops
- night-watch: 3 dep updates shipped, 1 rolled back (breaking change in `x-pkg@2.0`)
- health: all green except `mailpit` (degraded, non-critical)

### Strategy
- predict: new PR #42 overlaps with in-flight refactor in `/src/auth`

### Actions needed
- Review rollback from night-watch (ETA: 5 min)
- Decide on `x-pkg` pin strategy (open question on GitHub #41)

Rules

• MUST stay under 200 words unless the user explicitly asks for more detail
• MUST lead with decision-relevant facts, not chronology — "what should I act on" before "what happened"
• NEVER invent agent activity — report only what the logs show; absence of logs means "no data", not "nothing happened"
• CRITICAL: separate Ops (what ran) from Strategy (what was decided) from Actions (what needs a human) — mixing them defeats the brief
• MANDATORY: when no material activity exists for a category, omit the category heading instead of writing "none"

Gotchas

• kb/learnings/ often mixes drafts with completed entries. Filter by frontmatter status: final or by filename convention before aggregating.
• maintenance/ branch logs from /night-watch use a different format (Shift Report markdown) than agent run logs. Do not concatenate blindly — parse each source separately and normalize.
• "Recent runs" without an explicit time bound defaults to everything on some log backends. Always pass --since or a date filter, or you will read a week into yesterday's memory.
• Successful runs outnumber interesting runs by an order of magnitude. Aggressively filter green/noop entries — they are the signal's noise floor.

Token Receipts

The --tokens flag reports real token usage parsed from Claude Code session JSONL — not estimates. Useful for:

• Verifying output-mode: concise actually reduces tokens vs default sessions
• Spotting expensive runs before they show up on the bill
• Capturing a baseline before changing prompts or skills

Underlying script: scripts/session_token_stats.py.

# Aggregate current session
python3 scripts/session_token_stats.py --json

# Statusline-friendly one-line output
python3 scripts/session_token_stats.py --statusline

# Trend vs baseline
python3 scripts/session_token_stats.py --statusline --baseline ~/.softspark/ai-toolkit/baseline.json

Status line (installed by default in v3.2.0+)

ai-toolkit install wires ~/.claude/settings.json to app/hooks/ai-toolkit-statusline.sh. The hook reads native Claude Code statusLine stdin (no session JSONL parsing) and renders one line:

➜ <dir> git:(branch) ✗ ████░░░░░░ 43%  ↑6.5k ↓252k effort:xhigh <model>

Segments left to right:

• ➜ <dir> — current directory basename
• git:(branch) ✗ — git branch + dirty marker
• 10-cell progress bar for context-window usage. Color tiers: green <70%, orange 70–89%, red ≥90%
• ↑in ↓out — token arrows. Green up = input (upload), red down = output (download). Both cumulative across the session.
• effort:level — Claude Code effort level (low / medium / high / xhigh)
• model name

Custom statusLine entries you set yourself (without the _source: ai-toolkit tag) are preserved untouched on install.

Opt-outs (no reinstall required):

• AI_TOOLKIT_STATUSLINE_DISABLE=1 — silence the line entirely
• AI_TOOLKIT_STATUSLINE_NO_TOKENS=1 — hide token arrows segment
• AI_TOOLKIT_STATUSLINE_NO_GIT=1 — hide git segment
• AI_TOOLKIT_STATUSLINE_NO_EFFORT=1 — hide effort level segment
• AI_TOOLKIT_STATUSLINE_NO_COLOR=1 — disable ANSI colors
• AI_TOOLKIT_STATUSLINE_SHOW_COST=1 — append Claude Code's reported cost (cost.total_cost_usd)
• AI_TOOLKIT_STATUSLINE_DUMP=1 — write received stdin to /tmp/cc-statusline-input.json (debug)

Save a baseline

python3 scripts/session_token_stats.py --json | jq '.totals' > ~/.softspark/ai-toolkit/baseline.json
export AI_TOOLKIT_STATUSLINE_BASELINE=~/.softspark/ai-toolkit/baseline.json

The statusline then renders trend arrows (↑ / ↓) against that baseline.

When NOT to Use

• For a specific production incident — use /workflow incident-response
• For one-agent activity detail — read that agent's logs directly (kb/learnings/<agent>/)
• For planning future work — use /plan or /prd-to-plan
• For a technical system-up/down status — use /health
• When no agents have produced logs in the window — say so and stop; do not pad