Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Spawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.
.claude/skills/sediman-agent-reflect/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 20% | 0% |
Mine the current conversation for durable learnings, then route them into skill edits.
Skip when the conversation is trivial, off-topic, or already covered by an existing skill the parent followed correctly. One-offs are not learnings.
The parent finds its own transcript file before fanning out. The system prompt names the active workspace's agent-transcripts/ directory; use that path. Do not glob across ~/.cursor/projects/*/. That crosses workspace boundaries and reads private chats from unrelated projects.
bashls -t <agent-transcripts>/*.jsonl <agent-transcripts>/*/*.jsonl <agent-transcripts>/*/subagents/*.jsonl 2>/dev/null | head -10
Three transcript layouts: legacy flat (<id>.jsonl), current nested (<id>/<id>.jsonl), and subagent (<parent>/subagents/<child>.jsonl).
For each candidate, read the first JSONL line and check that message.content[0].text contains the conversation's opening user prompt. Take the matching path. If no path resolves, write a tight digest of the session and pass that instead.
One message, three Task calls, subagent_type: generalPurpose, explicit model: on each, agent mode (readonly: false). Reviewers need MCP access for context lookups (tickets, chat threads, observability traces referenced in the transcript); readonly strips MCPs. The prompt forbids file writes; the parent applies edits.
| Lens | model | Prompt template | |---|---|---| | Judgment | claude-opus-4-8-thinking-xhigh | references/judgment-reviewer.md | | Tooling | composer-2.5-fast | references/tooling-reviewer.md | | Divergent | claude-opus-4-8-thinking-xhigh | references/divergent-reviewer.md |
Pass each template verbatim, substituting the transcript path or digest where marked. Reviewers return findings in the Task response body.
One Task call, subagent_type: generalPurpose, model: claude-opus-4-8-thinking-xhigh, agent mode (readonly: false). The synthesizer's quality check includes spot-verifying citations, which can require MCP access; readonly strips MCPs. Use references/synthesizer.md verbatim, with each reviewer's full output inlined where marked. The synthesizer returns a structured Accepted / Rejected / Backlog list.
Sanity-check the synthesizer's Accepted list. For any item that would be enforced more reliably by a lint rule, script, metadata flag, or runtime check, move it from Accepted to Backlog. The synthesizer already applies this criterion; this is a final pass before edits land. See the encode-lessons-in-structure principle skill.
Before applying any Accepted edit, present the synthesizer's full Accepted/Rejected/Backlog output to the user and wait for explicit approval. The user picks which subset to apply and may redirect routings. Skill changes affect every future agent in the org; do not auto-apply.
Backlog items file to whatever devex / backlog tracker your team uses automatically. Those are tracker submissions, not skill edits. Only the Accepted list waits for approval.
For each approved Accepted item, follow the Routing field exactly:
create-skill skill and run its draft / test / iterate loop.tune description: <skill path> (the skill exists but didn't trigger when it should have): hand to create-skill and run its description-optimization loop.new skill via create-skill: <kebab-name>: hand creation to create-skill. Do not invent the shape ad hoc.If your environment ships a SKILL.md validator, run it on every touched skill before declaring done. Skip this step if it doesn't.
Short list, no preamble:
<skill path>. What changed, one line each.<skill path>. One line each (rare).<issue title> (<tags>). One line each.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,556 | 5,450 | +20% | 1 | 1 | 0% | 676 | 1,463 | +116% | 0 | 0 | — |
case-02 | fail→fail | 9,300 | 4,309 | -54% | 1 | 1 | 0% | 1,546 | 1,337 | -14% | 0 | 0 | — |
case-03 | fail→fail | 10,796 | 5,255 | -51% | 1 | 1 | 0% | 1,683 | 1,394 | -17% | 0 | 0 | — |
case-04 | pass→fail | 12,751 | 6,055 | -53% | 1 | 1 | 0% | 2,339 | 1,448 | -38% | 0 | 0 | — |
case-05 | fail→fail | 3,244 | 4,494 | +39% | 1 | 1 | 0% | 274 | 1,331 | +386% | 0 | 0 | — |
case-06 | fail→fail | 6,149 | 5,008 | -19% | 1 | 1 | 0% | 889 | 1,433 | +61% | 0 | 0 | — |
case-07 | fail→fail | 9,731 | 7,322 | -25% | 1 | 1 | 0% | 1,903 | 1,618 | -15% | 0 | 0 | — |
case-08 | fail→fail | 14,962 | 1,965 | -87% | 1 | 1 | 0% | 2,318 | 1,455 | -37% | 0 | 0 | — |
case-09 | fail→fail | 8,705 | 6,071 | -30% | 1 | 1 | 0% | 1,571 | 2,419 | +54% | 0 | 0 | — |
case-10 | fail→pass | 13,080 | 6,193 | -53% | 1 | 1 | 0% | 2,174 | 2,249 | +3% | 0 | 0 | — |
case-11 | fail→fail | 11,029 | 6,473 | -41% | 1 | 1 | 0% | 1,986 | 1,500 | -24% | 0 | 0 | — |
case-12 | fail→pass | 9,613 | 4,891 | -49% | 1 | 1 | 0% | 1,505 | 1,825 | +21% | 0 | 0 | — |
case-13 | fail→fail | 4,722 | 5,524 | +17% | 1 | 1 | 0% | 640 | 1,423 | +122% | 0 | 0 | — |
case-14 | pass→pass | 4,810 | 3,817 | -21% | 1 | 1 | 0% | 716 | 1,813 | +153% | 0 | 0 | — |
case-15 | fail→pass | 9,410 | 2,936 | -69% | 1 | 1 | 0% | 1,440 | 1,655 | +15% | 0 | 0 | — |
case-16 | fail→pass | 9,319 | 2,705 | -71% | 1 | 1 | 0% | 1,441 | 1,585 | +10% | 0 | 0 | — |
case-17 | fail→fail | 15,676 | 8,651 | -45% | 1 | 1 | 0% | 2,723 | 2,572 | -6% | 0 | 0 | — |
case-18 | fail→fail | 6,570 | 2,327 | -65% | 1 | 1 | 0% | 978 | 1,417 | +45% | 0 | 0 | — |
case-23 | fail→fail | 2,587 | 7,511 | +190% | 1 | 1 | 0% | 392 | 2,228 | +468% | 0 | 0 | — |
case-19 | fail→pass | 9,254 | 3,252 | -65% | 1 | 1 | 0% | 1,405 | 1,685 | +20% | 0 | 0 | — |
case-20 | fail→pass | 6,863 | 4,122 | -40% | 1 | 1 | 0% | 1,198 | 1,778 | +48% | 0 | 0 | — |
case-21 | pass→pass | 4,353 | 2,684 | -38% | 1 | 1 | 0% | 655 | 1,581 | +141% | 0 | 0 | — |
case-22 | fail→fail | 12,334 | 6,075 | -51% | 1 | 1 | 0% | 1,814 | 2,073 | +14% | 0 | 0 | — |
case-24 | pass→pass | 8,275 | 3,695 | -55% | 1 | 1 | 0% | 1,212 | 1,643 | +36% | 0 | 0 | — |
case-25 | fail→pass | 9,812 | 4,279 | -56% | 1 | 1 | 0% | 1,429 | 1,787 | +25% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted, and 16 counted toward the lift figure. The other 9 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +24 percentage points is the difference between those two pass rates over the 16 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.