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feat: integrate learnings + confidence into 9 skill templates

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Add {{LEARNINGS_SEARCH}}, {{LEARNINGS_LOG}}, and {{CONFIDENCE_CALIBRATION}}
placeholders to review, ship, plan-eng-review, plan-ceo-review, office-hours,
investigate, retro, and cso templates. Regenerated all SKILL.md files.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-03-28 22:55:08 -07:00
parent ef487a9fd4
commit 870586c946
33 changed files with 677 additions and 0 deletions
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33
retro/SKILL.md
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@@ -59,6 +59,15 @@ for _PF in $(find ~/.gstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null
fi
break
done
# Learnings count
eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)" 2>/dev/null || true
_LEARN_FILE="${GSTACK_HOME:-$HOME/.gstack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
_LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
echo "LEARNINGS: $_LEARN_COUNT entries loaded"
else
echo "LEARNINGS: 0"
fi
```
If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
@@ -621,6 +630,30 @@ For each contributor (including the current user), compute:
**If there are Co-Authored-By trailers:** Parse `Co-Authored-By:` lines in commit messages. Credit those authors for the commit alongside the primary author. Note AI co-authors (e.g., `noreply@anthropic.com`) but do not include them as team members — instead, track "AI-assisted commits" as a separate metric.
## Capture Learnings
If you discovered a non-obvious pattern, pitfall, or architectural insight during
this session, log it for future sessions:
```bash
~/.claude/skills/gstack/bin/gstack-learnings-log '{"skill":"retro","type":"TYPE","key":"SHORT_KEY","insight":"DESCRIPTION","confidence":N,"source":"SOURCE","files":["path/to/relevant/file"]}'
```
**Types:** `pattern` (reusable approach), `pitfall` (what NOT to do), `preference`
(user stated), `architecture` (structural decision), `tool` (library/framework insight).
**Sources:** `observed` (you found this in the code), `user-stated` (user told you),
`inferred` (AI deduction), `cross-model` (both Claude and Codex agree).
**Confidence:** 1-10. Be honest. An observed pattern you verified in the code is 8-9.
An inference you're not sure about is 4-5. A user preference they explicitly stated is 10.
**files:** Include the specific file paths this learning references. This enables
staleness detection: if those files are later deleted, the learning can be flagged.
**Only log genuine discoveries.** Don't log obvious things. Don't log things the user
already knows. A good test: would this insight save time in a future session? If yes, log it.
### Step 10: Week-over-Week Trends (if window >= 14d)
If the time window is 14 days or more, split into weekly buckets and show trends:

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retro/SKILL.md.tmpl
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@@ -277,6 +277,8 @@ For each contributor (including the current user), compute:
**If there are Co-Authored-By trailers:** Parse `Co-Authored-By:` lines in commit messages. Credit those authors for the commit alongside the primary author. Note AI co-authors (e.g., `noreply@anthropic.com`) but do not include them as team members — instead, track "AI-assisted commits" as a separate metric.
{{LEARNINGS_LOG}}
### Step 10: Week-over-Week Trends (if window >= 14d)
If the time window is 14 days or more, split into weekly buckets and show trends: