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v1.33.1.0 fix(learnings): token-OR query + task-shaped retrieval in 3 long skills (#1442)

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* fix(learnings): use token-OR matching in gstack-learnings-search --query

Split the query on whitespace into tokens; a learning matches if ANY
token appears as a substring in ANY of key/insight/files. Previously
the whole query was a single substring, so multi-word queries like
"debug investigation" only matched learnings whose insight contained
that exact contiguous phrase, which is usually nothing.

Whitespace-only query falls through to no-query (matches today's no-flag
behavior). Single-word queries behave exactly as before.

Adds test/gstack-learnings-search.test.ts: 3 assertions covering
multi-token, single-token, and no-query backwards compat.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* feat(resolver): parameterized LEARNINGS_SEARCH with shell-injection guard

The {{LEARNINGS_SEARCH}} macro now accepts a query=KEYWORD argument that
gets interpolated as --query "<keyword>" into the generated bash. Empty
value falls through to no-query (principle of least surprise: a stray
{{LEARNINGS_SEARCH:query=}} placeholder gets today's behavior, not a
build failure). Pattern reuses the parameterized-macro parsing from
composition.ts. The 13 templates that don't pass a query stay
byte-identical in their generated SKILL.md output.

Shell-injection guard: the query value is whitelisted to
^[A-Za-z0-9 _-]+$ at gen-skill-docs time. Any \$(), backticks,
semicolons, or quotes throw a loud build error instead of emitting
executable bash. Static template queries are safe by inspection;
this defends against future contributors writing dangerous values.

Adds 5 assertions to test/gen-skill-docs.test.ts covering no-args,
claude+query=foo bar on both cross-project and project-scoped branches,
codex host variant, empty value semantics, and shell-injection payloads
(\$(whoami), backticks, ;, &, ", \\, \$x) throwing build errors.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* feat(skills): task-shaped queries + mid-flow refresh in /investigate /qa /ship

The three long skills now pull learnings keyed to their theme at the
top, then re-pull at phase boundaries as work shifts to new sub-tasks.

Top-of-skill queries (5-6 token unions, token-OR matched):
- investigate: "debug investigation root cause hypothesis bug fix"
- qa: "qa testing bug regression flake fixture"
- ship: "release ship version changelog merge pr"

Mid-flow refresh blocks (concrete keyword recipe + worked examples):
- investigate: between Phase 1 (hypothesis) and Phase 2 (analysis),
  keyed to the hypothesis noun. Examples: auth-cookie, session-expiry.
- qa: between Phase 7 (triage) and Phase 8 (fix loop), keyed to the
  buggy component name. Examples: checkout-button, signup-form.
- ship: just before Step 12 (VERSION bump), keyed to the headline
  feature. Examples: learnings-search, pacing, worktree-ship.

Keyword recipe enforces alphanumeric+hyphen only (no quotes, slashes,
dots, colons) so dynamic queries cannot inject shell metacharacters.

The other 13 short-lived skills keep the bare {{LEARNINGS_SEARCH}} form.
Backwards-compat verified via diff: their generated SKILL.md output is
byte-identical to before this change.

Golden ship fixtures regenerated to match the new ship/SKILL.md output.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore: bump version and changelog (v1.33.1.0)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* test: refresh codex+factory ship golden fixtures

Follow-up to 513c9660 — the codex and factory host outputs needed
regeneration too, missed in the initial commit because gen:skill-docs
was only run for the claude host. Now matches gen:skill-docs --host all.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-05-11 19:34:33 -07:00
committed by GitHub
parent d21ba06b5a
commit 1a4f0c9c15
16 changed files with 365 additions and 27 deletions
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18
test/fixtures/golden/claude-ship-SKILL.md vendored
View File

@@ -1824,9 +1824,9 @@ Search for relevant learnings from previous sessions:
_CROSS_PROJ=$(~/.claude/skills/gstack/bin/gstack-config get cross_project_learnings 2>/dev/null || echo "unset")
echo "CROSS_PROJECT: $_CROSS_PROJ"
if [ "$_CROSS_PROJ" = "true" ]; then
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 --cross-project 2>/dev/null || true
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 --query "release ship version changelog merge pr" --cross-project 2>/dev/null || true
else
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 2>/dev/null || true
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 --query "release ship version changelog merge pr" 2>/dev/null || true
fi
```
@@ -2459,6 +2459,20 @@ already knows. A good test: would this insight save time in a future session? If
### Refresh learnings for the headline feature on this branch
The top-of-skill learnings pull was keyed to "release ship" broadly. Before the VERSION/CHANGELOG step, re-pull learnings keyed to THIS branch's headline feature so any prior version-bump or CHANGELOG pitfalls for similar features surface.
Pick ONE keyword that names the headline feature you're shipping. The keyword should be a noun: the primary skill or module name, the central feature noun, or the binary you changed. The keyword MUST be alphanumeric or hyphen only — no quotes, slashes, dots, colons, or whitespace. If your candidate has any of those, simplify to just the alphanumeric stem.
Worked examples (ship-specific): good keywords are `learnings-search`, `pacing`, `worktree-ship`. Bad: `the branch headline`, `v1.31.1.0`, `feat: token-or search`.
```bash
~/.claude/skills/gstack/bin/gstack-learnings-search --query "<your-keyword>" --limit 5 2>/dev/null || true
```
If any learnings come back, name which one applies to the version bump or CHANGELOG framing in one sentence. If none come back, continue without reference — the absence is itself useful information.
## Step 12: Version bump (auto-decide)
**Idempotency check:** Before bumping, classify the state by comparing `VERSION` against the base branch AND against `package.json`'s `version` field. Four states: FRESH (do bump), ALREADY_BUMPED (skip bump), DRIFT_STALE_PKG (sync pkg only, no re-bump), DRIFT_UNEXPECTED (stop and ask).

37
test/fixtures/golden/codex-ship-SKILL.md vendored
View File

@@ -310,6 +310,26 @@ Effort both-scales: when an option involves effort, label both human-team and CC
Net line closes the tradeoff. Per-skill instructions may add stricter rules.
12. **Non-ASCII characters — write directly, never \u-escape.** When any
string field (question, option label, option description) contains
Chinese (繁體/簡體), Japanese, Korean, or other non-ASCII text, emit
the literal UTF-8 characters in the JSON string. **Never escape them
as `\uXXXX`.** Claude Code's tool parameter pipe is UTF-8 native
and passes characters through unchanged. Manually escaping requires
recalling each codepoint from training, which is unreliable for long
CJK strings — the model regularly emits the wrong codepoint (e.g.
writes `\u3103` thinking it is 管 U+7BA1, but `\u3103` is
actually ㄃, so the user sees `管理工具` rendered as `㄃3用箱`).
The trigger is long, multi-line questions with hundreds of CJK
characters: that is exactly when reflexive escaping kicks in and
exactly when miscoding is most damaging. Long ≠ escape. Keep
characters literal.
Wrong: `"question": "請選擇\uXXXX\uXXXX\uXXXX\uXXXX"`
Right: `"question": "請選擇管理工具"`
Only JSON-mandatory escapes remain allowed: `\n`, `\t`, `\"`, `\\`.
### Self-check before emitting
Before calling AskUserQuestion, verify:
@@ -322,6 +342,7 @@ Before calling AskUserQuestion, verify:
- [ ] Dual-scale effort labels on effort-bearing options (human / CC)
- [ ] Net line closes the decision
- [ ] You are calling the tool, not writing prose
- [ ] Non-ASCII characters (CJK / accents) written directly, NOT \u-escaped
## Artifacts Sync (skill start)
@@ -1789,7 +1810,7 @@ Add a `## Verification Results` section to the PR body (Step 19):
Search for relevant learnings from previous sessions on this project:
```bash
$GSTACK_BIN/gstack-learnings-search --limit 10 2>/dev/null || true
$GSTACK_BIN/gstack-learnings-search --limit 10 --query "release ship version changelog merge pr" 2>/dev/null || true
```
If learnings are found, incorporate them into your analysis. When a review finding
@@ -2053,6 +2074,20 @@ already knows. A good test: would this insight save time in a future session? If
### Refresh learnings for the headline feature on this branch
The top-of-skill learnings pull was keyed to "release ship" broadly. Before the VERSION/CHANGELOG step, re-pull learnings keyed to THIS branch's headline feature so any prior version-bump or CHANGELOG pitfalls for similar features surface.
Pick ONE keyword that names the headline feature you're shipping. The keyword should be a noun: the primary skill or module name, the central feature noun, or the binary you changed. The keyword MUST be alphanumeric or hyphen only — no quotes, slashes, dots, colons, or whitespace. If your candidate has any of those, simplify to just the alphanumeric stem.
Worked examples (ship-specific): good keywords are `learnings-search`, `pacing`, `worktree-ship`. Bad: `the branch headline`, `v1.31.1.0`, `feat: token-or search`.
```bash
$GSTACK_ROOT/bin/gstack-learnings-search --query "<your-keyword>" --limit 5 2>/dev/null || true
```
If any learnings come back, name which one applies to the version bump or CHANGELOG framing in one sentence. If none come back, continue without reference — the absence is itself useful information.
## Step 12: Version bump (auto-decide)
**Idempotency check:** Before bumping, classify the state by comparing `VERSION` against the base branch AND against `package.json`'s `version` field. Four states: FRESH (do bump), ALREADY_BUMPED (skip bump), DRIFT_STALE_PKG (sync pkg only, no re-bump), DRIFT_UNEXPECTED (stop and ask).

39
test/fixtures/golden/factory-ship-SKILL.md vendored
View File

@@ -312,6 +312,26 @@ Effort both-scales: when an option involves effort, label both human-team and CC
Net line closes the tradeoff. Per-skill instructions may add stricter rules.
12. **Non-ASCII characters — write directly, never \u-escape.** When any
string field (question, option label, option description) contains
Chinese (繁體/簡體), Japanese, Korean, or other non-ASCII text, emit
the literal UTF-8 characters in the JSON string. **Never escape them
as `\uXXXX`.** Claude Code's tool parameter pipe is UTF-8 native
and passes characters through unchanged. Manually escaping requires
recalling each codepoint from training, which is unreliable for long
CJK strings — the model regularly emits the wrong codepoint (e.g.
writes `\u3103` thinking it is 管 U+7BA1, but `\u3103` is
actually ㄃, so the user sees `管理工具` rendered as `㄃3用箱`).
The trigger is long, multi-line questions with hundreds of CJK
characters: that is exactly when reflexive escaping kicks in and
exactly when miscoding is most damaging. Long ≠ escape. Keep
characters literal.
Wrong: `"question": "請選擇\uXXXX\uXXXX\uXXXX\uXXXX"`
Right: `"question": "請選擇管理工具"`
Only JSON-mandatory escapes remain allowed: `\n`, `\t`, `\"`, `\\`.
### Self-check before emitting
Before calling AskUserQuestion, verify:
@@ -324,6 +344,7 @@ Before calling AskUserQuestion, verify:
- [ ] Dual-scale effort labels on effort-bearing options (human / CC)
- [ ] Net line closes the decision
- [ ] You are calling the tool, not writing prose
- [ ] Non-ASCII characters (CJK / accents) written directly, NOT \u-escaped
## Artifacts Sync (skill start)
@@ -1794,9 +1815,9 @@ Search for relevant learnings from previous sessions:
_CROSS_PROJ=$($GSTACK_BIN/gstack-config get cross_project_learnings 2>/dev/null || echo "unset")
echo "CROSS_PROJECT: $_CROSS_PROJ"
if [ "$_CROSS_PROJ" = "true" ]; then
$GSTACK_BIN/gstack-learnings-search --limit 10 --cross-project 2>/dev/null || true
$GSTACK_BIN/gstack-learnings-search --limit 10 --query "release ship version changelog merge pr" --cross-project 2>/dev/null || true
else
$GSTACK_BIN/gstack-learnings-search --limit 10 2>/dev/null || true
$GSTACK_BIN/gstack-learnings-search --limit 10 --query "release ship version changelog merge pr" 2>/dev/null || true
fi
```
@@ -2429,6 +2450,20 @@ already knows. A good test: would this insight save time in a future session? If
### Refresh learnings for the headline feature on this branch
The top-of-skill learnings pull was keyed to "release ship" broadly. Before the VERSION/CHANGELOG step, re-pull learnings keyed to THIS branch's headline feature so any prior version-bump or CHANGELOG pitfalls for similar features surface.
Pick ONE keyword that names the headline feature you're shipping. The keyword should be a noun: the primary skill or module name, the central feature noun, or the binary you changed. The keyword MUST be alphanumeric or hyphen only — no quotes, slashes, dots, colons, or whitespace. If your candidate has any of those, simplify to just the alphanumeric stem.
Worked examples (ship-specific): good keywords are `learnings-search`, `pacing`, `worktree-ship`. Bad: `the branch headline`, `v1.31.1.0`, `feat: token-or search`.
```bash
$GSTACK_ROOT/bin/gstack-learnings-search --query "<your-keyword>" --limit 5 2>/dev/null || true
```
If any learnings come back, name which one applies to the version bump or CHANGELOG framing in one sentence. If none come back, continue without reference — the absence is itself useful information.
## Step 12: Version bump (auto-decide)
**Idempotency check:** Before bumping, classify the state by comparing `VERSION` against the base branch AND against `package.json`'s `version` field. Four states: FRESH (do bump), ALREADY_BUMPED (skip bump), DRIFT_STALE_PKG (sync pkg only, no re-bump), DRIFT_UNEXPECTED (stop and ask).