msd-core/gsd-core/workflows/graduation.md

graduation.md — LEARNINGS.md Cross-Phase Graduation Helper

Invoked by: transition.md step graduation_scan. Never invoked directly by users.

This workflow clusters recurring items across the last N phases' LEARNINGS.md files and surfaces promotion candidates to the developer via HITL. No item is promoted without explicit developer approval.

---

Configuration

Read from project config (config.json):

Key	Default	Description
features.graduation	true	Master on/off switch. false skips silently.
features.graduation_window	5	How many prior phases to scan
features.graduation_threshold	3	Minimum cluster size to surface

---

Step 1: Guard Checks

_GSD_SHIM_NAME="gsd-tools.cjs"; _GSD_RUNTIME_ROOT="${RUNTIME_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}"; GSD_TOOLS="${_GSD_RUNTIME_ROOT}/gsd-core/bin/${_GSD_SHIM_NAME}"; _gsd_at() { for _p; do if [ -f "$_p" ]; then GSD_TOOLS="$_p"; return 0; fi; done; return 1; }; _gsd_id_ok() { case "$("$1" runtime-identity --raw 2>/dev/null || true)" in '{"packageName":"@opengsd/gsd-core"'*'}') return 0;; *) return 1;; esac; }; _gsd_homes() { _gsd_at "${CLAUDE_CONFIG_DIR:-$HOME/.claude}/gsd-core/bin/${_GSD_SHIM_NAME}" "${HERMES_HOME:-$HOME/.hermes}/gsd-core/bin/${_GSD_SHIM_NAME}" "${CURSOR_CONFIG_DIR:-$HOME/.cursor}/gsd-core/bin/${_GSD_SHIM_NAME}" "${CODEX_HOME:-$HOME/.codex}/gsd-core/bin/${_GSD_SHIM_NAME}" "${GEMINI_CONFIG_DIR:-$HOME/.gemini}/gsd-core/bin/${_GSD_SHIM_NAME}" "${COPILOT_CONFIG_DIR:-$HOME/.copilot}/gsd-core/bin/${_GSD_SHIM_NAME}" "${WINDSURF_CONFIG_DIR:-$HOME/.codeium/windsurf}/gsd-core/bin/${_GSD_SHIM_NAME}" "${AUGMENT_CONFIG_DIR:-$HOME/.augment}/gsd-core/bin/${_GSD_SHIM_NAME}" "${TRAE_CONFIG_DIR:-$HOME/.trae}/gsd-core/bin/${_GSD_SHIM_NAME}" "${QWEN_CONFIG_DIR:-$HOME/.qwen}/gsd-core/bin/${_GSD_SHIM_NAME}" "${CODEBUDDY_CONFIG_DIR:-$HOME/.codebuddy}/gsd-core/bin/${_GSD_SHIM_NAME}" "${CLINE_CONFIG_DIR:-$HOME/.cline}/gsd-core/bin/${_GSD_SHIM_NAME}" "${GROK_AGENTS_HOME:-$HOME/.agents}/gsd-core/bin/${_GSD_SHIM_NAME}" "${ANTIGRAVITY_CONFIG_DIR:-$HOME/.gemini/antigravity}/gsd-core/bin/${_GSD_SHIM_NAME}" "${OPENCODE_CONFIG_DIR:-${XDG_CONFIG_HOME:-$HOME/.config}/opencode}/gsd-core/bin/${_GSD_SHIM_NAME}" "${KILO_CONFIG_DIR:-${XDG_CONFIG_HOME:-$HOME/.config}/kilo}/gsd-core/bin/${_GSD_SHIM_NAME}"; }; if _gsd_at "${_GSD_RUNTIME_ROOT}/gsd-core/bin/${_GSD_SHIM_NAME}" "${_GSD_RUNTIME_ROOT}/.claude/gsd-core/bin/${_GSD_SHIM_NAME}" "${_GSD_RUNTIME_ROOT}/.codex/gsd-core/bin/${_GSD_SHIM_NAME}"; then gsd_run() { node "$GSD_TOOLS" "$@"; }; elif _gsd_homes; then gsd_run() { node "$GSD_TOOLS" "$@"; }; elif unset -f gsd_run; _G="$(command -v gsd_run)"; [ -n "$_G" ] && _gsd_id_ok "$_G"; then GSD_TOOLS="$_G"; gsd_run() { "$GSD_TOOLS" "$@"; }; else echo "ERROR: gsd-tools.cjs not found at $GSD_TOOLS and no identity-proving gsd_run is on PATH. Run: npx -y @opengsd/gsd-core@latest --claude --local" >&2; exit 1; fi; GSD_IDENTITY_STATUS=unverified; _gsd_id_ok gsd_run && GSD_IDENTITY_STATUS=ok; export GSD_IDENTITY_STATUS; [ "$GSD_IDENTITY_STATUS" = ok ] || echo "WARNING: \"$GSD_TOOLS\" did not prove it is @opengsd/gsd-core - it is either a different package or an @opengsd/gsd-core older than the runtime-identity verb. See docs/how-to/diagnose-a-foreign-gsd-tools.md" >&2; if [ -n "${CLAUDE_ENV_FILE:-}" ] && [ -n "${GSD_TOOLS:-}" ]; then printf "export PATH='%s':\"\$PATH\"\n" "${GSD_TOOLS%/*}" >> "$CLAUDE_ENV_FILE" 2>/dev/null || true; fi
RESPONSE_LANGUAGE=$(gsd_run query config-get response_language --raw --default "" 2>/dev/null || echo "")
GRADUATION_ENABLED=$(gsd_run query config-get features.graduation --raw 2>/dev/null || echo "true")
GRADUATION_WINDOW=$(gsd_run query config-get features.graduation_window --raw 2>/dev/null || echo "5")
GRADUATION_THRESHOLD=$(gsd_run query config-get features.graduation_threshold --raw 2>/dev/null || echo "3")

If response_language is set: All user-facing output of this workflow — narration between tool calls, status updates, progress notes, findings, questions, prompts, and explanations — MUST be presented in {response_language}. Technical terms, code, file paths, and subagent prompts stay in English — only user-facing output is translated.

Skip silently (print nothing) if:

• features.graduation is false
• Fewer than graduation_threshold completed prior phases exist (not enough data)

Skip silently (print nothing) if total items across all LEARNINGS.md files in the window is fewer than 5.

---

Step 2: Collect LEARNINGS.md Files

Find LEARNINGS.md files from the last N completed phases (excluding the phase currently completing):

find .planning/phases -name "*-LEARNINGS.md" | sort | tail -n "$GRADUATION_WINDOW"

For each file found:

1. Parse the four category sections: ## Decisions, ## Lessons, ## Patterns, ## Surprises
2. Extract each ### Item Title + body as a single item record: { category, title, body, source_phase, source_file }
3. Skip items that already contain **Graduated:** — they have been promoted and must not re-surface

---

Step 3: Cluster by Lexical Similarity

For each category independently, cluster items using Jaccard similarity on tokenized title+body:

Tokenization: lowercase, strip punctuation, split on whitespace, remove stop words (a, an, the, is, was, in, on, at, to, for, of, and, or, but, with, from, that, this, by, as).

Jaccard similarity: |A ∩ B| / |A ∪ B| where A and B are token sets. Two items are in the same cluster if similarity ≥ 0.25.

Clustering algorithm: single-pass greedy — process items in phase order; add to the first cluster whose centroid (union of all cluster tokens) has similarity ≥ 0.25 with the new item; otherwise start a new cluster.

Cluster size filter: only surface clusters with distinct source phases ≥ graduation_threshold (not just total items — same item repeated in one phase still counts as 1 distinct phase).

---

Step 4: Check graduation_backlog in STATE.md

Read .planning/STATE.md graduation_backlog section (if present). Format:

graduation_backlog:
  - cluster_id: "{sha256-of-cluster-title}"
    status: "dismissed"   # or "deferred"
    deferred_until: "phase-N"  # only for deferred entries
    cluster_title: "{representative title}"

Skip any cluster whose cluster_id matches a dismissed entry.

Skip any cluster whose cluster_id matches a deferred entry where deferred_until phase has not yet completed.

---

Step 5: Surface Promotion Candidates

For each qualifying cluster, determine the suggested target file:

Category	Suggested Target
decisions	PROJECT.md — append under ## Validated Decisions (create section if absent)
patterns	PATTERNS.md — append under the appropriate category section (create file if absent)
lessons	PROJECT.md — append under ## Invariants (create section if absent)
surprises	Flag for human review — if genuinely surprising 3+ times, something structural is wrong

Print the graduation report:

📚 Graduation scan across phases {M}–{N}:

  HIGH RECURRENCE ({K}/{WINDOW} phases)
  ├─ Cluster: "{representative title}"
  ├─ Category: {category}
  ├─ Sources: {list of NN-LEARNINGS filenames}
  └─ Suggested target: {target file} § {section}

  [repeat for each qualifying cluster, ordered HIGH→LOW recurrence]

For each cluster above, choose an action:
  P = Promote now   D = Defer (re-surface next transition)   X = Dismiss (never re-surface)   A = Defer all remaining

---

Step 6: HITL — Process Each Cluster

For each cluster (in order from Step 5), ask the developer:

Cluster: "{title}" [{category}, {K} phases] → {target}
Action [P/D/X/A]:

Use AskUserQuestion (or equivalent HITL primitive for the current runtime). If TEXT_MODE is true, display the cluster question as plain text and accept typed input. Accept single-character input: P, D, X, A (case-insensitive).

On P (Promote now):

1. Read the target file (or create it with a standard header if absent)
2. Append the cluster entry under the suggested section:

   ### {Cluster representative title}
   {Merged body — combine unique sentences across cluster items}
   
   **Sources:** Phase {A}, Phase {B}, Phase {C}
   **Promoted:** {ISO_DATE}
   

3. For each source LEARNINGS.md item in the cluster, append **Graduated:** {target-file}:{ISO_DATE} after its last existing field
4. Commit both the target file and all annotated LEARNINGS.md files in a single atomic commit: docs(learnings): graduate "{cluster title}" to {target-file}

On D (Defer):

Write to .planning/STATE.md under graduation_backlog:

- cluster_id: "{sha256}"
  status: "deferred"
  deferred_until: "phase-{NEXT_PHASE_NUMBER}"
  cluster_title: "{title}"

On X (Dismiss):

Write to .planning/STATE.md under graduation_backlog:

- cluster_id: "{sha256}"
  status: "dismissed"
  cluster_title: "{title}"

On A (Defer all):

Defer the current cluster (same as D) and skip all remaining clusters for this run, deferring each to the next transition. Print:

[graduation: deferred all remaining clusters to next transition]

Then proceed directly to Step 7.

---

Step 7: Completion Report

After processing all clusters, print:

Graduation complete: {promoted} promoted, {deferred} deferred, {dismissed} dismissed.

If no clusters qualified (all filtered by backlog or threshold), print:

[graduation: no qualifying clusters in phases {M}–{N}]

---

First-Run Behaviour

On the first transition after upgrading to a version that includes this workflow, all extant LEARNINGS.md files may produce a large batch of candidates at once. A [Defer all] shorthand is available: if the developer enters A at any cluster prompt, all remaining clusters for this run are deferred to the next transition.

---

No-Op Conditions (silent skip)

• features.graduation = false
• Fewer than graduation_threshold prior phases with LEARNINGS.md
• Total items < 5 across the window
• All qualifying clusters are in graduation_backlog as dismissed