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Get Started Free →Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through
.claude/skills/mikeyobrien-ralph-docs/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 194% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 88% | 0% |
Introspect Ralph Orchestrator the same way the framework expects a smart agent to — consult the published documentation map at <https://mikeyobrien.github.io/ralph-orchestrator/llms.txt>, fetch only the sections relevant to the question, and answer from authoritative sources.
Use this skill to behave like an internal Ralph contributor rather than a guess-first assistant.
memories, tasks, presets, backends, CLI, TUI, diagnostics, waves, or API.
max_iterations?", "why didn't my hat fire?") from first principles in the docs, not pattern-matching.
ralph-orchestrator codebase —locating the right crate, the relevant concept doc, and the existing test surface before writing code.
concept + reference docs for that subsystem, identify the probable file path in the repo.
Getting Started pages instead of the agent's stale training data.
Ralph's llms.txt is a curated map, not a full-text dump. The workflow is:
llms.txt to see the top-level sections(Getting Started / Concepts / User Guide / Advanced / API / Examples / Contributing / Reference).
links directly to .md versions — those are agent-optimized and should be preferred over scraping HTML.
speculatively; the budget should be spent on answering, not browsing.
paths are listed in AGENTS.md / CLAUDE.md inside the ralph-orchestrator checkout).
verify.
references/llms-txt-map.md (hats, event loop, memories, tasks, backends, presets, CLI, TUI, diagnostics, waves, API).
~/.cache/ralph-docs/llms.txt exists and is <7 days old, use it. Elserefetch it:
bash mkdir -p ~/.cache/ralph-docs curl -sSfL https://mikeyobrien.github.io/ralph-orchestrator/llms.txt \ -o ~/.cache/ralph-docs/llms.txt
.md pages most relevant to the subsystem. The mapentries are documented in references/llms-txt-map.md; use it to shortcut the grep.
curl -sSfL <url> -o ~/.cache/ralph-docs/<stem>.mdand read them. Agents with web_fetch or an equivalent tool should use that instead.
relevant sentence when the user asks "does Ralph do X?" so they can audit.
checkout and follow references/contributing.md for the propose-a-change workflow.
ralph-hats. For operating a live loop (running, resuming, merging, debugging), defer to ralph-loop. For code changes to ralph-orchestrator itself, this skill scopes the change; the actual editing uses the agent's native code-editing tools.
not surface an answer, say so and suggest checking the source tree at <https://github.com/mikeyobrien/ralph-orchestrator>.
CLI flags, preset names). Ralph's CLI evolves; always verify against guide/cli-reference.md or reference/changelog.md.
.md URLs from llms.txt over scraping the rendered HTML.~/.cache/ralph-docs/ with a 7-day stalenessthreshold. Refetch llms.txt before any other doc to detect renames/moves.
installed ralph version), note the mismatch and suggest ralph --version so the user can decide which to trust.
concepts/tenets/index.md first. Ralph's six tenets are load-bearing; changes that fight them usually belong somewhere else.
references/contributing.md for the crate map), the concept doc that justifies the change, and the test file that should cover it.
references/llms-txt-map.md
references/common-questions.md
references/contributing.md
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 3,592 | 5,086 | +42% | 1 | 1 | 0% | 618 | 1,775 | +187% | 0 | 0 | — |
case-02 | fail→fail | 12,102 | 13,127 | +8% | 1 | 1 | 0% | 2,228 | 4,106 | +84% | 0 | 0 | — |
case-03 | fail→fail | 10,671 | 5,363 | -50% | 1 | 1 | 0% | 1,706 | 1,738 | +2% | 0 | 0 | — |
case-04 | fail→fail | 16,920 | 7,360 | -57% | 1 | 1 | 0% | 3,544 | 1,809 | -49% | 0 | 0 | — |
case-05 | fail→fail | 6,559 | 7,187 | +10% | 1 | 1 | 0% | 528 | 1,890 | +258% | 0 | 0 | — |
case-06 | pass→fail | 2,463 | 8,511 | +246% | 1 | 1 | 0% | 396 | 3,118 | +687% | 0 | 0 | — |
case-07 | fail→pass | 3,974 | 3,088 | -22% | 1 | 1 | 0% | 691 | 2,032 | +194% | 0 | 0 | — |
case-08 | pass→pass | 8,564 | 3,077 | -64% | 1 | 1 | 0% | 1,471 | 2,036 | +38% | 0 | 0 | — |
case-09 | fail→pass | 8,475 | 3,765 | -56% | 1 | 1 | 0% | 1,524 | 2,096 | +38% | 0 | 0 | — |
case-15 | fail→pass | 7,324 | 4,602 | -37% | 1 | 1 | 0% | 1,342 | 2,318 | +73% | 0 | 0 | — |
case-10 | fail→pass | 10,536 | 3,135 | -70% | 1 | 1 | 0% | 1,693 | 2,031 | +20% | 0 | 0 | — |
case-11 | fail→pass | 6,465 | 3,748 | -42% | 1 | 1 | 0% | 1,126 | 2,114 | +88% | 0 | 0 | — |
case-12 | pass→pass | 8,175 | 4,594 | -44% | 1 | 1 | 0% | 1,610 | 2,274 | +41% | 0 | 0 | — |
case-13 | pass→pass | 8,164 | 3,147 | -61% | 1 | 1 | 0% | 1,286 | 1,979 | +54% | 0 | 0 | — |
case-14 | fail→pass | 10,059 | 10,180 | +1% | 1 | 1 | 0% | 1,716 | 2,509 | +46% | 0 | 0 | — |
case-16 | fail→pass | 5,518 | 2,428 | -56% | 1 | 1 | 0% | 989 | 1,832 | +85% | 0 | 0 | — |
case-17 | pass→pass | 6,243 | 3,565 | -43% | 1 | 1 | 0% | 1,156 | 2,128 | +84% | 0 | 0 | — |
case-18 | fail→pass | 12,398 | 8,771 | -29% | 1 | 1 | 0% | 2,003 | 2,884 | +44% | 0 | 0 | — |
case-19 | pass→pass | 6,953 | 2,494 | -64% | 1 | 1 | 0% | 1,103 | 1,763 | +60% | 0 | 0 | — |
case-20 | fail→pass | 9,724 | 4,649 | -52% | 1 | 1 | 0% | 1,587 | 2,245 | +41% | 0 | 0 | — |
case-21 | fail→pass | 3,310 | 4,070 | +23% | 1 | 1 | 0% | 498 | 2,183 | +338% | 0 | 0 | — |
case-22 | fail→pass | 4,036 | 2,916 | -28% | 1 | 1 | 0% | 741 | 1,909 | +158% | 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. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 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 +45 percentage points is the difference between those two pass rates over the 18 comparable cases. 3 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.