learn-eval · diff
git:20260822.0b72f86 to git:20260824.8c93955
33 added, 15 removed. Audit A to A.
---
name: learn-eval
- description: "Extract reusable patterns from the session, self-evaluate quality before saving, and determine the right save location (Global vs Project)."
+ description: "Extract a reusable pattern from the current session, judge it Save / Improve then Save / Absorb / Drop against a grounding checklist, and route every Save to a destination something actually reaches — absorbed into an existing skill / rule / doc section, or promoted to a real skill via skill-creator. Use when the user says 「今回の学びを残して」「learn-eval して」 or /learn-eval. There is no notes parking lot: if nothing would route to it, the verdict is Drop. NOT for mining past sessions (session-judgment-mining), auditing skills (skill-stocktake), or distilling rules (rules-distill)."
compatibility: Developed and tested on Claude Code; portable to other Agent Skills-compatible agents.
user-invocable: true
origin: shimo4228
---
# /learn-eval - Extract, Evaluate, then Save
Extends the full /learn flow with a pre-save quality evaluation and a save-location decision.
## What to Extract
Look for:
1. **Error Resolution Patterns** — root cause + fix + reusability
2. **Debugging Techniques** — non-obvious steps, tool combinations
3. **Workarounds** — library quirks, API limitations, version-specific fixes
4. **Project-Specific Patterns** — conventions, architecture decisions, integration patterns
## Process
1. Review the session for extractable patterns
2. Identify the most valuable/reusable insight
- 3. **Determine save location:**
- - Ask: "Would this pattern be useful in a different project?"
- - **Global** (`~/.claude/skills/learned/`): general-purpose patterns useful across 2+ projects (bash compatibility, LLM API behavior, debugging techniques, etc.)
- - **Project** (`.claude/skills/learned/` in current project): knowledge specific to this project (quirks of a particular config file, project-specific architecture decisions, etc.)
- - When in doubt, choose Global (moving it into a project later is easier than the reverse)
- - 正本: [`docs/adr/0025-global-vs-project-asset-placement.md`](../../docs/adr/0025-global-vs-project-asset-placement.md)。この節はその learned-skill 向け実例（旧正本の `rules/common/skills.md` は ADR-0035 の rules 縮退で origin schema のみに縮退した）
+ 3. **Determine the destination — there is no parking lot.**
+ `learned/` was retired on 2026-08-23 (ADR-0047). Every Save must land somewhere that
+ something actually routes to, so pick one of exactly two:
+
+ - **Absorb into an existing asset** — the pattern belongs inside a skill, rule, or
+ `hooks/README.md` section that already owns the topic. Name the file and the section.
+ This is the default: an addition to a reachable asset beats a new file.
+ - **Promote to a skill** — the pattern has its own independent trigger (a user request
+ that no installed skill answers). Run skill: **skill-creator** (required by
+ `rules/common/skills.md` before writing any skill).
+
+ If neither fits, the verdict is **Drop**, not "park it somewhere for now". A note that
+ nothing points at is reachable only by grep, and grep requires already knowing the
+ content exists — measured over 74 days, the retired `learned/` directory was read
+ during real work 12 times across 8 notes, while the audits that judged whether to keep
+ it accounted for 161 of its 184 reads.
+
+ Global vs project placement (once a destination type is chosen): 正本は
+ [`docs/adr/0025-global-vs-project-asset-placement.md`](../../docs/adr/0025-global-vs-project-asset-placement.md)。
+
4. Draft the skill file using this format:
```markdown
---
name: pattern-name
description: "Description in 130 characters or less"
user-invocable: false
origin: auto-extracted
---
# [Descriptive Pattern Name]
**Extracted:** [Date]
**Context:** [Brief description of when this applies]
## Problem
[What problem this solves - be specific]
## Solution
[The pattern/technique/workaround - with code examples]
## When to Use
[Trigger conditions]
```
5. **Quality gate — checklist + holistic verdict**
#### 5a. Mandatory checklist (verify by actually reading the files)
Run **all** of the following before evaluating the draft:
- [ ] Grepped `~/.claude/skills/` for keywords and checked for content overlap
- [ ] Checked for overlap with MEMORY.md (project + global)
- [ ] Considered appending to an existing skill instead (see knowledge-placement-decision)
- [ ] Confirmed the pattern is reusable, not a one-off fix
- [ ] Checked the pattern against the **session's observational record** (actual tool output, errors, user corrections). Is it grounded in "what actually happened" rather than your own summary or paraphrase?
Then, **generate and answer 3–5 draft-specific atomic yes/no questions**.
The fixed checklist covers harness-invariant checks (duplication, reusability) but
does not test the draft's own claims (what it states under Problem / Solution /
When to Use), so this step fills that gap:
- **Atomicity**: each question tests exactly one verifiable claim
- **Refutation-oriented**: phrase questions to seek disconfirmation, not to affirm
the draft as written. Examples: "Does the code example run as-is in the stated
environment?" "Is the trigger condition observable from the prompt text of a
future session?" "Which line of the session's observational record does the
Solution correspond to?"
- **No aggregation**: answers are Yes/No + one line of evidence. Never convert them
into a numeric score (e.g. a satisfaction ratio). The only consumed output is the
verdict in 5b; binary answers serve strictly as its evidence
#### 5b. Holistic verdict
Weigh the checklist results, the binary answers, and the draft together, then choose
exactly **one** of the following. **Always enumerate the No-answered questions as
grounds for the verdict** (hidden Nos breed verdict drift):
| Verdict | Meaning | Next action |
|---------|---------|---------------|
| **Save** | Unique, concrete, well-scoped | Go to Step 6 |
| **Improve then Save** | Valuable but needs fixes | No questions = improvement items → fix → re-judge with the same questions (once only) |
| **Absorb into [X]** | Should be appended to an existing skill | Present the target and the content to add → go to Step 6 |
| **Drop** | Trivial, redundant, or abstract | Explain why and stop |
**Guiding dimensions** (reference points for judgment, not a scoring rubric):
- **Concreteness / actionability**: has code examples/commands, immediately usable
- **Scope fit**: name, trigger, and content align; focused on a single pattern
- **Uniqueness**: given the checklist results, provides value existing knowledge cannot
- **Reusability**: will realistically be triggered in future sessions
- **Grounding**: is the source an observational record (what actually happened) or
your own interpretation/summary? Self-evaluation-only loops drift (your paraphrase
gets re-fixed as fact), so lean toward Drop for extractions not grounded in
observation. **If a grounding question is No, lean Drop even when everything else
is Yes** (never let averaging dilute a dominant No)
**Improve then Save improvement list**: the No-answered questions become the
improvement items as-is. For each No, write one line on what to change to make it a
Yes; after fixing, re-judge with the **same question set** (once only — do not
regenerate the questions: if the bar moves, you cannot tell whether the fix worked
or the bar loosened).
6. **Per-verdict confirmation flow (one at a time, `[y/n/skip]`)**
Even when multiple patterns were extracted from the session, confirm them
**one at a time — never ask for batch approval** (follows config-gc's confirm-each
design; a bulk "save them all? [y/n]" is banned).
For each candidate, present the evidence first (checklist results + verdict
rationale), then ask `[y/n/skip]`.
The user can stop at any point. `n` = discard, `skip` = defer for now (leave a
one-line reason):
- **Save**: present the save path + checklist results + one-line verdict rationale + the full draft → save after `[y/n/skip]` confirmation
- **Absorb into [X]**: present the target path + the content to add (as a diff) + checklist results + verdict rationale → append after `[y/n/skip]` confirmation
- **Drop**: show the checklist results + reason only (no confirmation needed; stop)
- 7. Save / Absorb to the determined location
+ 7. Save to the destination chosen in Step 3
- 8. **Promotion check (after a Save only)**
+ - **Absorb**: edit the named asset in place and show the diff. Do not create a file.
+ - **Promote**: hand the draft to skill: **skill-creator** — it fixes the intent packet,
+ draws the boundary against neighbouring skills, structures it as
+ `~/.claude/skills/<name>/SKILL.md`, and passes it through a fresh-context draft gate
+ (learn-eval = extraction and Save/Drop judgment / skill-creator = shape, boundary and
+ gate — a deliberate role split).
- `learned/` is a flat directory of `.md` files and does not participate in
- `skills/<name>/SKILL.md`-style discovery. That means description-based automatic
- triggering never fires — the file remains a passive reference note found only by
- grep. After the save completes, ask the user exactly once:
+ 8. **Reachability check (after a Save only)**
- - **Leave it in learned/** (default) — sufficient as reference material / a grep target. If the confirmation gets no response, choose this
- - **Promote to an active skill** — when description-triggered automatic application should apply in future sessions. Run skill: **skill-creator** on the learned draft — it fixes the intent packet, draws the boundary against neighbouring skills, structures it as `~/.claude/skills/<name>/SKILL.md`, and passes it through a fresh-context draft gate (learn-eval = extraction and Save/Drop judgment / skill-creator = shape, boundary and gate — a deliberate role split). After promotion, delete the `learned/` file so nothing is managed twice
+ State in one line what will route to the saved content in a future session: the section
+ it now lives in, or the skill description that will select it. **If the honest answer is
+ "nothing — someone would have to grep for it", the Save was wrong**; go back to Step 3
+ and either absorb it into a reachable asset or Drop it.
## Output Format for Step 5
```
### Checklist
- [x] skills/ grep: no overlap (or overlap found → details)
- [x] MEMORY.md: no overlap (or overlap found → details)
- [x] Append-to-existing considered: new file appropriate (or should append to [X])
- [x] Reusability: confirmed (or one-off → Drop)
### Draft-specific questions
- [Yes] Q1: ... — one-line evidence
- [No] Q2: ... — one-line evidence → (on Improve: one-line fix plan)
### Verdict: Save / Improve then Save / Absorb into [X] / Drop
**Rationale:** (1–2 sentences explaining the verdict; always mention any No questions)
```
## Notes
- Don't extract trivial fixes (typos, simple syntax errors)
- Don't extract one-time issues (specific API outages, etc.)
- Focus on patterns that will save time in future sessions
- Keep skills focused — one pattern per skill
- On an Absorb verdict, do not create a new file — append to the existing skill instead
## References
The generic design canon for this evaluation style (binary checks as evidence →
holistic named verdict, no aggregation) is the `llm-as-judge` skill; Step 5 is its
N=1 implementation.
Design rationale for Step 5's two-layer design (binary question decomposition → holistic verdict):
- BinEval — "Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement" ([arXiv:2606.27226](https://arxiv.org/abs/2606.27226)). A framework that decomposes evaluation criteria into atomic yes/no questions and wires failed questions directly into improvement feedback. The dynamic generation of draft-specific questions and the "No questions = improvement items" path are ported from here
- The same checklist-style evaluation research line: CheckEval (arXiv:2403.18771), TICK (arXiv:2410.03608), FActScore (arXiv:2305.14251), UniEval (arXiv:2210.07197)
- The decision **not** to adopt numeric scores (satisfaction ratios) also follows BinEval's own limitations: on subjective, holistic quality dimensions, over-decomposition degrades correlation with human judgment, and the proportion of affirmed questions does not map linearly to quality. For an N=1 draft evaluation the only consumed output is the verdict; binary answers serve strictly as its evidence