Agent skill
speaking
Golden path /speak — generate RLM-isolated follow-ups with chronicle temporal context injection.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/speaking-0xhoneyjar-construct-beehive
SKILL.md
/speak — Generate Follow-Ups with Temporal Context
Golden path command that generates RLM-isolated follow-up messages with chronicle temporal context. Queries the chronicle for events around each user's last feedback date and injects them into the subagent prompt.
Chain
- Preflight:
gp_check_chronicle(advisory),gp_check_growth_dir - Cognition check: for each candidate user, check cognition file status (fresh/stale/missing)
- Chronicle query: per user, query
chronicle-query.sh --around {last_feedback_date} --window {chronicle_window_days} - Follow-up generation: invoke
/follow-uptruename with cognition + chronicle context (Step 2c.5 + 2g.6) - Status + suggest next (include cognition coverage in status)
Execution
source scripts/observer/golden-path-lib.sh
gp_status_header "speak"
has_chronicle=false
if gp_check_chronicle; then
has_chronicle=true
gp_status_ok "chronicle" "temporal context available"
else
gp_status_skip "chronicle" "generating without temporal context"
fi
if ! gp_check_growth_dir; then
gp_status_fail "growth" "no growth files — run /listen first"
fi
# Step 1: Cognition check + follow-up generation per user
generated=0
fresh_count=0
stale_count=0
missing_count=0
for canvas in $(gp_list_canvases); do
user=$(basename "$canvas" .md)
# Check cognition status for this user
cognition_path="grimoires/observer/cognition/${user}.yaml"
if [[ -f "$cognition_path" ]]; then
# /follow-up Step 2c.5 handles staleness check + auto-refresh
# Here we just report pre-generation status for the status line
fresh_count=$((fresh_count + 1)) # /follow-up will reclassify if stale
else
missing_count=$((missing_count + 1))
fi
# Get last feedback date from canvas frontmatter
last_feedback_date="" # Extract from canvas
# Query chronicle if available
chronicle_context=""
if [[ "$has_chronicle" == "true" && -n "$last_feedback_date" ]]; then
chronicle_context=$(scripts/observer/chronicle-query.sh \
--around "$last_feedback_date" \
--window "$GP_WINDOW_DAYS" \
--summary 2>/dev/null) || true
fi
# Invoke /follow-up with cognition + chronicle context
# Cognition loading is handled by /follow-up Step 2c.5 (auto-refresh if stale)
# Chronicle context is injected via Step 2g.6
generated=$((generated + 1))
done
if [[ "$generated" -gt 0 ]]; then
gp_status_ok "follow-ups" "${generated} follow-ups generated"
else
gp_status_skip "follow-ups" "no users need follow-ups"
fi
gp_status_ok "cognition" "${fresh_count} fresh, ${stale_count} auto-refreshed, ${missing_count} ad-hoc"
gp_progression_summary
gp_suggest_next "speak"
gp_status_footer
Chronicle Integration (Step 2g.6)
When invoking the /follow-up truename, the /speak orchestrator injects temporal context into the per-user subagent prompt:
## Temporal Context (from chronicle)
The following events occurred near this user's last feedback:
{chronicle_context}
Use these events to explain score movements and contextualize follow-up questions.
If a score changed after a release, reference the specific release.
This section is added after the canvas data and growth state, before the subagent generates the follow-up message.
Truenames Chained
| Truename | Purpose |
|---|---|
/follow-up |
RLM-isolated follow-up generation |
chronicle-query.sh |
Temporal context for ±N days |
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