skill-evolve

name: skill-evolve description: Refine, create, or retire your own skills based on recurring patterns from past sessions tools: [bash, read_file, write_file, edit_file, sub_agent] core: true origin: creator

Skill Evolution

You are evolving your own skills. This is the only skill that modifies other skills. Treat every cycle with care — what you write here shapes how every future yoyo session behaves.

When to use

Only when invoked via scripts/skill_evolve.sh. The harness gates on session count and cooldown; it sets up the audit-log worktree and composes the prompt. Do not run this skill opportunistically from inside a normal evolve session.

Hard rules (read first, every cycle)

These three rules cannot be violated. Each cycle either honors all three or writes a refused event and exits.

HARD RULE #1 — Eligible targets only (allow-list)

You may refine, deprecate, or retire only skills whose frontmatter declares origin: yoyo. Any other value, OR a missing origin: field, means the skill is off-limits. This is an allow-list: silence means "don't touch."

Three categories of skill exist:

Today the eligible set is exactly the skills whose SKILL.md declares origin: yoyo:

• social
• family
• release
• any skill you previously spawned (which inherit origin: yoyo from the Create template)

Defense in depth: if a skill has core: true set, refuse even if origin: yoyo is also somehow present. The two flags should never co-occur, but the conservative move is to honor the deny-flag.

If a recurring pattern suggests a non-eligible skill needs change (e.g., a core skill, or an installed marketplace skill), do not edit it. Instead, write a learning to memory/learnings.jsonl with source: "skill-evolve" and a clear pattern_key, and append a meta-suggestion block to skills/_journal.md. The human creator will decide.

HARD RULE #2 — Never edit yourself

You must NEVER modify skills/skill-evolve/SKILL.md. If you believe this skill needs improvement, append a meta-suggestion block to skills/_journal.md and stop:

## evt-XXXX meta-suggestion
- ts: <ISO8601>
- target: skills/skill-evolve/SKILL.md
- suggestion: <one-paragraph description>

HARD RULE #3 — One mutation per cycle

Each cycle produces exactly one of:

• a refinement diff (one skill, ≤30 added lines, ≤15 removed)
• a candidate skill draft (one new directory)
• a retirement (one git mv to skills_attic/)
• a NO-OP event (you found nothing worth doing)

If you find yourself wanting to do two things, pick the one with the strongest evidence and write the second to memory/learnings.jsonl for next cycle.

HARD RULE #4 — Refine and Create events must declare an expected outcome

Every refine and create event in skills/_journal.md MUST include an expected: line — a freeform prose commitment naming (a) a concrete observable signal that should change, (b) a horizon (e.g. "within ~5 sessions" or "by next cycle"), and (c) a fallback move if the prediction does not hold.

If you cannot articulate all three, the edit is not justified by evidence: NO-OP the cycle instead of committing a refine/create without an expected: line. This is decision-observability discipline (paper: arxiv 2604.25850) at the cognitive layer — there is no validator, but a future cycle re-reads the line as informal evidence and a human reads it as an audit trail.

expected: is forbidden on retire, revive, meta-suggestion, refused, NO-OP, and init events (they do not ship a behavioral change, so there is nothing to predict).

The body of the line is freeform prose. See "Step 7 — append the event" for the template position and worked examples; see "What an expected: line must do (and must not be)" later in this document for the anti-patterns to refuse.

Glossary

• session — one run of scripts/evolve.sh (the main evolution loop). There are ~3 per day.
• cycle — one run of this skill, invoked from scripts/skill_evolve.sh. Cycles are gated by a session-counter and a 24h cooldown, so they fire roughly once every 5+ sessions.
• real cycle — a cycle that produced one of refine | create | retire | meta-suggestion. Excludes init, refused, and NO-OP.

Bootstrap (first three real cycles only)

We are mid-life, not at Day 1, so the cold-start rules from the original design are softened — but the first three real cycles still get extra constraints to let the loop settle.

To know which cycle you are in, count the non-init, non-refused, non-NO-OP entries in skills/_journal.md:

cycle_index=$(grep -E '^## .*evt-[0-9]+ (refine|create|retire|meta-suggestion)' skills/_journal.md | wc -l)
# cycle_index=0 → this is the first real cycle
# cycle_index=1 → second
# cycle_index=2 → third
# cycle_index>=3 → full lifecycle unlocked

• First real cycle (cycle_index == 0): only refine or NO-OP allowed. Do not create. Do not retire.
• Second real cycle (cycle_index == 1): refine, create, or NO-OP. No retirement yet.
• Third real cycle onward (cycle_index >= 2): full lifecycle unlocked (refine | create | retire | NO-OP).

(Note: the gate-counter at .skill_evolve_counter is unrelated to this — it just controls when the cycle fires, not what it can do.)

Lifecycle states

Every eligible skill carries a status: field in its frontmatter. Five states. Important: yoagent always loads anything with a valid <dir>/SKILL.md regardless of status — status: is your bookkeeping, telling you what to do next, not what the loader does. The only way to fully un-load a skill from the agent's prompt is to git mv its directory to skills_attic/ (sibling of skills/, not scanned by --skills).

The [CANDIDATE — unreviewed] prefix is agent-written when you Create a skill (see Create template below). Nothing in the loader injects it. It tells future sessions to treat the skill as experimental.

Cycle execution sequence

Run these steps in order, every cycle.

1. Read evidence

# Latest cycles (note prior rejections — eval-result: regression/tie — so you don't re-propose them; R2b rechecks before any refine):
tail -n 200 skills/_journal.md

# Recent self-reflection:
tail -n 50 memory/learnings.jsonl

# Top of journal (newest entries are at top):
head -n 200 journals/JOURNAL.md

# Recent runs:
gh run list --json url,conclusion,createdAt,name -L 10 || echo "[]"

# Audit evidence (set by harness, points at audit-log worktree):
ls "${YOYO_AUDIT_DIR:-/tmp/audit-read/sessions}" 2>/dev/null | tail -30

First-run handling: if $YOYO_AUDIT_DIR is unset or its directory is empty, the audit-log branch hasn't accumulated evidence yet (this is normal on the first 1–2 cycles). In that case:

• Skip the per-session audit.jsonl mining in step 3 ("Mine patterns").
• Use only memory/learnings.jsonl and journals/JOURNAL.md for complaint and use signals.
• Lean toward NO-OP — without audit evidence, scoring is too noisy to support a confident refine/create/retire decision.
• Write the NO-OP event with note: evidence: only learnings (audit-log unavailable).

2. Enumerate eligible skills

# Allow-list: only skills declaring origin: yoyo are eligible.
# Defense in depth: also exclude anything carrying core: true.
for d in skills/*/; do
    name=$(basename "$d")
    [ "$name" = "skill-evolve" ] && continue
    [ -f "$d/SKILL.md" ] || continue
    grep -q "^core: true" "$d/SKILL.md" && continue
    grep -q "^origin: yoyo$" "$d/SKILL.md" || continue
    echo "$name"
done

3. Mine patterns

This step has two layers: counting (the basic signals) and diagnosing (understanding why failures happened, not just that they did). Diagnosis is what turns recurrence into actionable refinement targets.

3a. Count basic signals

For each eligible skill, count:

• Complaint signals: entries in memory/learnings.jsonl whose pattern_key or title/takeaway mentions the skill and uses negative language ("wrong", "didn't", "instead", "should have").
• Failure signals: tool-call failures in ${YOYO_AUDIT_DIR}/day-*/audit.jsonl where the bash command or args reference the skill's domain.
• Use signals: number of sessions where any string from the skill's frontmatter keywords: list appears in that session's audit.jsonl. This is uses.
• Win signals: out of those sessions, count the ones where outcome.json has test_ok: true AND tasks_succeeded >= 1. This is wins.
• Applied signals (issue #501): for a candidate pattern_key, the number of distinct sessions whose outcome.json.applied_pattern_keys contains that key. This is applied — the "acted on it" signal, distinct from uses (keyword merely present = recalled/exposed). applied is the honest "use after recall"; it gates promotion in step 4.3, not the EMA score (keep scores stable). scripts/skill_evolve_report.py surfaces it under "Session outcomes".

If a skill's frontmatter is missing keywords:, fall back to its name as the only keyword (likely noisy — flag in _journal.md so the operator can add proper keywords).

Compute wins/uses and update the EMA score:

new_score = 0.3 * blended + 0.7 * old_score
blended   = 0.5 * (wins/uses) + 0.3 * (1 - complaints/uses) + 0.2 * mention_rate

Update the skill's frontmatter with the new values: score, uses, wins, and last_used (= the timestamp of the most-recent matching session). These updates are part of your single allowed mutation per cycle — you may bundle them into a refine event, or write a tiny "score-update" event when nothing else changes (this counts as a NO-OP for the bootstrap counter).

3b. Diagnose the cause (trace-based)

Counting tells you which skill is struggling. Diagnosing tells you what to fix. Borrowed from the GEPA pattern (Genetic-Pareto Prompt Evolution): read the actual execution traces, don't just count failures.

For each skill where complaint_signals ≥ 2 OR (wins/uses) < 0.5 (with uses ≥ 3), open the relevant session's audit.jsonl and look for these failure-mode patterns:

For each candidate refinement target, write a 1-2 sentence cause hypothesis:

target: social
hypothesis: 3 sessions show repeated `gh api graphql` calls with malformed `categoryId`
            args (sessions day-52, day-55, day-57). Skill's Procedure mentions categoryId
            but doesn't show the format. Refinement: add a verbatim example.

Carry this hypothesis into step 4 (action selection) and step 5 (Refine — it tells you what to write in the diff). Without a hypothesis, you're guessing; with one, the refinement is targeted and the eval (Refine step R4) has something concrete to compare.

If no clear hypothesis emerges from the traces, prefer NO-OP over speculative refinement. Counting alone is not a license to mutate.

4. Pick exactly one action

Decision order (first match wins):

1. Retire (third cycle onward only): if any skill has score < 0.3 AND last_used ≥ 10 sessions ago, retire the lowest-scoring one. Skip if there are < 2 active eligible skills (don't bottom out the library).
2. Refine: if any skill (a) has complaint_signals ≥ 2, OR (b) has (wins/uses) < 0.5 with uses ≥ 3, AND in either case has not been refined in the last 3 sessions (last_evolved check), refine it. This matches the diagnosis-trigger condition in step 3b. Pick the target with the strongest evidence (highest complaint count, or lowest wins-ratio if no complaints). A refine may be subtractive: deleting or replacing stale/superseded guidance is as valid as adding, and a net-negative diff is often the best outcome — a skill should get sharper over time, not longer.
3. Create (second cycle onward only, and only if active skill count < 25): if any pattern_key appears in ≥3 distinct sessions of learnings.jsonl, AND a learning carrying that pattern_key has a validation_case (issue #501 — required: a pattern with no validation_case may not become a skill; leave it as a note, or write a learning suggesting one be authored), AND no existing eligible skill covers it (≥3 keyword overlap → refine that one instead), draft a new skill. Applied gate (bootstrap): also require applied ≥ 1 for that pattern_key (it was acted on, not just recurred); while applied_pattern_keys is still sparse (the first ~10 sessions after this field ships), recurrence ≥3 alone may stand in — once the signal is populated, tighten to require both. (Only the applied gate has a bootstrap; the validation_case requirement above is never relaxed.)
4. NO-OP: nothing meets the bars. Write a NO-OP event with a one-line note about what evidence you considered.

If you've written 3 consecutive NO-OP events, also write evolution_saturation: true to the event — the harness reads this and extends the cooldown.

5. Execute the action

Refine

Refinement uses a snapshot + A/B eval pattern (borrowed from Anthropic's skill-creator). The goal: never commit a refinement that doesn't measurably improve the skill on at least one concrete prompt.

Step R1 — Snapshot the baseline. Before editing, copy the current SKILL.md to a temp location:

mkdir -p /tmp/skill-evolve-baseline
cp "skills/<target>/SKILL.md" "/tmp/skill-evolve-baseline/<target>.SKILL.md"

Step R2 — Generate 2-3 synthetic test prompts. Read the target skill's ## When to use and ## Procedure sections. Derive concrete prompts a future agent might receive that should trigger this skill. Examples for social:

• "Reply to discussion #42 with a thoughtful response"
• "Post a 1-in-4-chance proactive riff in The Show category"
• "Find unanswered questions in the Journal Club category"

Write them to /tmp/skill-evolve-eval/<target>/prompts.json:

[
  {"id": "p1", "prompt": "...", "expects": "<one-sentence success criterion>"},
  {"id": "p2", "prompt": "...", "expects": "..."}
]

Step R2b — Check rejection history (don't rediscover bad ideas). Before writing a candidate diff, scan skills/_journal.md for a prior rejection of this same edit. A rejection is a NO-OP event whose eval-result: is regression/tie (R5) or whose eval-summary: says baseline-better … Reverted (R6):

grep -iE "eval-result: (regression|tie)|eval-summary:.*baseline-better" skills/_journal.md

This grep is a recall pre-filter — read each hit's event block and confirm it is genuinely a rejection of the same skill: and the same section/idea (not a passing refine that merely says "0 regressions"). If so, do not re-run the A/B — write a NO-OP citing that evt-NNNN (note: prior regression on <target>/<area>, evt-NNNN) and stop. Only proceed if the candidate is genuinely new or the skill has changed since the rejection.

Step R3 — Write the candidate diff. Use edit_file to apply your refinement. Constraints:

• ≤30 added lines, ≤15 removed lines (diff stat). Deleting or replacing stale/superseded guidance is a valid refinement (not only adding) — spend the removed-line budget deliberately; a sharper, shorter skill is a good outcome.
• Touch only the ## Pitfalls and ## Procedure sections (or the skill's "what to do" body) — never the top-level description:, never any frontmatter field except the four bookkeeping fields established in step 3a: score, uses, wins, last_used. (last_evolved is also updated, to today's date.)

Step R4 — A/B compare. For each test prompt, generate a 1-3 sentence summary of how each version (baseline, candidate) would handle the prompt — what tools the agent would call, what order, what the outcome would look like.

Two execution modes, in order of preference:

• Preferred (sub-agent A/B): if you have sub_agent available, dispatch two sub-agent calls in parallel:

  • Sub-agent A: read /tmp/skill-evolve-baseline/<target>.SKILL.md + the test prompt → output JSON {"summary": "...", "tool_sequence": ["bash", "edit_file", ...]}
  • Sub-agent B: same with the candidate file
  • Use the structured outputs to compare apples-to-apples.

• Fallback (single-agent sequential): if sub_agent isn't available or returned an error, read the baseline file, write a baseline summary; then read the candidate file, write a candidate summary. Be deliberate about not letting the candidate read bias the baseline read — write the baseline summary BEFORE looking at the candidate.

For each prompt, decide one of:

• candidate-better: candidate's procedure is more specific, addresses the prompt more directly
• tie: no meaningful difference
• baseline-better: regression — the refinement made things worse

Step R5 — Decide. Commit the refinement only if:

• 0 prompts came out baseline-better, AND
• At least 1 prompt came out candidate-better

Otherwise: revert the edit (cp /tmp/skill-evolve-baseline/<target>.SKILL.md skills/<target>/SKILL.md) and write a NO-OP event with eval-result: regression (or eval-result: tie).

Step R6 — Append eval summary to the _journal.md event. Add an eval-summary: field to the event:

- eval-summary: 2/2 prompts candidate-better, 0 regressions

Or for a NO-OP-after-eval:

- eval-summary: 1/2 baseline-better — refinement was a regression on prompt p2 ("..."). Reverted.

Create

Draft skills/<new-name>/SKILL.md:

---
name: <new-name>
description: "[CANDIDATE — unreviewed] <pushy one-line trigger description, ≤200 chars total>"
tools: [bash, read_file, ...]
origin: yoyo
status: candidate
score: 0.5
uses: 0
wins: 0
last_used: null
last_evolved: <today>
parent_pattern_key: <kebab-case verb.object>
keywords: ["<distinctive substring 1>", "<distinctive substring 2>", "..."]   # ≥3 strings that, if found in a session's audit.jsonl, indicate this skill was used
---

# <Title>

## When to use
<concrete trigger conditions>

## Quick reference
<one-screen cheat sheet>

## Procedure
<numbered steps>

## Pitfalls
<things that have gone wrong before>

## Verification
<how the skill knows it succeeded>

The [CANDIDATE — unreviewed] prefix is critical — it tells the agent in future sessions to treat the skill as experimental, not as system-prompt-grade truth.

Retire

git mv skills/<name>/ skills_attic/<name>/

Soft delete. Recoverable. If yoyo invokes the skill's domain again within 3 cycles, you may revive it (move back, reset score to 0.5).

6. Validate

Before committing, run all of these. If any fails, write refused and exit:

# YAML frontmatter parses (use python3 since yq may not be installed):
python3 -c "
import sys, re
content = open('skills/<name>/SKILL.md').read()
m = re.match(r'---\n(.*?)\n---\n', content, re.DOTALL)
assert m, 'no frontmatter'
fm = m.group(1)
assert len(fm) <= 1900, f'frontmatter too long: {len(fm)}'
# crude parse
for line in fm.splitlines():
    if line.strip() and ':' not in line:
        sys.exit(f'invalid line: {line}')
"

# Description ≤ 200 chars:
desc=$(grep '^description:' skills/<name>/SKILL.md | head -1 | sed 's/^description: *//')
[ "${#desc}" -le 200 ] || { echo "description too long"; exit 1; }

# Body token estimate (~ word count, ceiling 5000):
body_words=$(awk '/^---$/{n++; next} n>=2' skills/<name>/SKILL.md | wc -w)
[ "$body_words" -le 5000 ] || { echo "body too long"; exit 1; }

# Build still works (the meta-skill itself shouldn't break the build, but defense in depth):
cargo build --release 2>&1 | tail -5

7. Append the event to skills/_journal.md

Get the next event number:

last=$(grep -oE 'evt-[0-9]+' skills/_journal.md | sort -u | tail -1)
n=$((${last#evt-} + 1))
evt=$(printf 'evt-%04d' $n)

Append (using >>, never overwrite):

## <ISO8601> <evt-NNNN> <type>
- skill: <name or "-">
- trigger: <one-line summary of evidence>
- diff: <+A -B (path)> or "n/a"
- validation: <pass | reason for refusal>
- score-delta: <old> → <new>
- parent-event: <evt-NNNN>
- expected: <observable signal | horizon | fallback>          # required for refine/create only; forbidden on all other types
- note: <optional one-line>

Where <type> is one of: init, refine, create, retire, revive, meta-suggestion, refused, NO-OP.

What an expected: line must do (and must not be)

A good expected: line names all three of: a concrete observable signal, a horizon, and a fallback move.

Concrete observables you may reference:

• A skill's frontmatter uses / wins / score (e.g. "social.uses should grow by ≥3 over the next 5 sessions")
• A specific failure cluster's recurrence in audit-log sessions (e.g. "the gh-discussion-comment STUCK cluster should drop to 0 hits within 5 sessions")
• A trace pattern from step 3b (e.g. "the git checkout revert-after-edit pattern on social/SKILL.md should not recur in the next 3 sessions")
• A concrete tool-call sequence that should/should not appear in audit.jsonl

Horizons: "by next cycle", "within ~3 sessions", "within ~5 sessions", "within 7 days". Do not say "eventually" or omit the horizon.

Fallbacks: name the next move if the prediction does not hold. Examples: "...otherwise this is a sub-skill candidate, not a prose refine"; "...otherwise the description: is the wrong target — try refining the body instead"; "...otherwise retire the skill".

Worked examples:

For a refine event:

- expected: STUCK rate on the gh-discussion-comment cluster should drop to 0
  within the next ~5 evolve sessions; if not, the prose tweak was insufficient
  and a helper script (sub-skill) is the right next step

For a create event:

- expected: at least 2 sessions in the next 5 should match this skill's
  keywords[] AND have outcome.json.test_ok=true (i.e. wins ≥ 2 by next cycle);
  if uses < 2 by then, the description: is too narrow and needs widening, or
  the pattern was a one-off and the skill should retire

Anti-patterns to refuse (these do not satisfy HARD RULE #4 — NO-OP instead of writing them):

• "feels better"
• "will be more readable"
• "the prose is now clearer"
• "users will like it"
• "yoyo will use this skill more" (no horizon, no signal)
• "this should help" (no horizon, no signal, no fallback)

If your candidate expected: line reads like one of those, you do not have a theory of impact — the evidence does not justify a mutation this cycle. Write NO-OP and move on.

8. Commit

git add skills/ skills_attic/ memory/learnings.jsonl
git commit -m "skill-evolve: <type> <skill-name>" || true

The harness pushes (or doesn't, depending on its config). Do not push from inside this skill.

Anti-bloat ceilings

Before any create action, verify all of these:

• Active skill count (any with status: active or status: refined) ≤ 25 before this create. If at the limit, you must retire first or write NO-OP.
• Total skill count in skills/ (excluding any skill with core: true) ≤ 30.
• The new skill's frontmatter is ≤ 1900 chars.
• The new skill's description is ≤ 200 chars (including the [CANDIDATE — unreviewed] prefix).
• The new skill's body is ≤ 5000 words.
• No existing eligible skill has ≥3 keyword overlap with the new skill's When to use section. If so, refine that skill instead.

Failure modes you must guard against

What good looks like

A healthy skills/_journal.md after 30 days:

• 4–10 events total (you don't run every session, and most cycles are NO-OP)
• Mix of refine (50%), create (10%), retire (10%), NO-OP (30%)
• Zero refused: self-edit or refused: core-edit events (your hard rules are holding)
• Per-skill EMA scores trending up or stable (not down)
• pattern_key recurrence dispersal falling over time — yoyo is internalizing patterns, not re-discovering them

If you see thrashing, score decay, or many refusals, write a meta-suggestion and let the human creator tighten the loop.