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Get Started Free →Build a graph-structured dossier on a seed entity via parallel fan-out + recursive expansion across web, memory, knowledge-graph, codebase, ADR index, and git intel
.claude/skills/ruvnet-dossier-collect/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-24 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-02 | ✓→✗ | ▼ Worse | -75% | 0% |
| case-03 | ✓→✗ | ▼ Worse | -76% | 0% |
Recursive parallel investigation that builds a graph-structured dossier on a seed entity.
You have a seed (a username, file, symbol, ADR-id, URL, or concept) and want to expand outward discovering every connected entity, with provenance per claim — rather than answering a specific question.
For specific questions use deep-research. For multi-step plans use goal-plan.
username (handle), file (path), symbol (code identifier), adr (ADR-NNN), url, or concept (free text).mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start with task dossier:<slug>.username: WebSearch, WebFetch on github.com/<user>, mcp__plugin_ruflo-core_ruflo__memory_search_unifiedadr: Read ADR file, Grep references, mcp__plugin_ruflo-core_ruflo__memory_search namespace adrsymbol: Grep, Glob, mcp__plugin_ruflo-core_ruflo__embeddings_search--exact is unset, also drop entities whose embedding cosine similarity ≥ 0.92 to an existing node.--max-breadth per source), recurse to step 4 until depth ≥ --max-depth OR budget exhausted.{ nodes, edges } graph. Each node carries { id, type, attrs, sources: [...] }. Each edge carries { from, to, kind, source, confidence }.<slug>.md — executive summary, entity table, mermaid graph, source-provenance footnotes<slug>.json — machine-readable graphv3/docs/examples/dossiers/<slug>/mcp__plugin_ruflo-core_ruflo__memory_store namespace dossier key <slug>.mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end with success status.json{ "seed": "ruvnet", "seedType": "username", "depth": 2, "truncated": false, "generatedAt": "ISO-8601", "nodes": [ { "id": "ruvnet", "type": "username", "attrs": { "...": "..." }, "sources": ["WebSearch", "github.com"] } ], "edges": [ { "from": "ruvnet", "to": "ruflo", "kind": "owns", "source": "github.com", "confidence": "high" } ], "stats": { "nodesByType": {}, "sourcesUsed": [], "tokensSpent": 0 } }
--budget-usd is set, track approximate cost via trajectory. On exhaustion: emit partial dossier with truncated: true and the entities still queued./ruflo-goals:dossier-collect ruvnet
/ruflo-goals:dossier-collect ADR-097 --max-depth 1
/ruflo-goals:dossier-collect "src/memory/hnsw.ts" --sources codebase,git,memory
/ruflo-goals:dossier-collect "ruflo-goals" --max-breadth 5 --budget-usd 1| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 21,470 | 6,439 | -70% | 1 | 1 | 0% | 4,683 | 1,614 | -66% | 0 | 0 | — |
case-02 | pass→fail | 26,638 | 6,235 | -77% | 1 | 1 | 0% | 6,221 | 1,565 | -75% | 0 | 0 | — |
case-03 | pass→fail | 27,861 | 5,441 | -80% | 1 | 1 | 0% | 6,209 | 1,483 | -76% | 0 | 0 | — |
case-04 | pass→fail | 17,204 | 5,281 | -69% | 1 | 1 | 0% | 2,653 | 1,342 | -49% | 0 | 0 | — |
case-05 | pass→pass | 16,647 | 25,247 | +52% | 1 | 1 | 0% | 2,997 | 4,899 | +63% | 0 | 0 | — |
case-06 | pass→pass | 7,664 | 10,204 | +33% | 1 | 1 | 0% | 1,606 | 3,307 | +106% | 0 | 0 | — |
case-07 | pass→pass | 13,687 | 9,636 | -30% | 1 | 1 | 0% | 2,305 | 2,580 | +12% | 0 | 0 | — |
case-08 | pass→pass | 10,840 | 2,170 | -80% | 1 | 1 | 0% | 1,809 | 1,345 | -26% | 0 | 0 | — |
case-09 | pass→pass | 13,111 | 4,620 | -65% | 1 | 1 | 0% | 2,257 | 1,820 | -19% | 0 | 0 | — |
case-10 | pass→pass | 7,543 | 1,940 | -74% | 1 | 1 | 0% | 1,167 | 1,261 | +8% | 0 | 0 | — |
case-11 | pass→pass | 9,951 | 3,267 | -67% | 1 | 1 | 0% | 1,673 | 1,564 | -7% | 0 | 0 | — |
case-12 | pass→pass | 13,615 | 3,698 | -73% | 1 | 1 | 0% | 2,223 | 1,605 | -28% | 0 | 0 | — |
case-13 | pass→pass | 6,798 | 6,922 | +2% | 1 | 1 | 0% | 1,229 | 2,296 | +87% | 0 | 0 | — |
case-14 | pass→pass | 13,797 | 3,857 | -72% | 1 | 1 | 0% | 2,224 | 1,628 | -27% | 0 | 0 | — |
case-15 | pass→pass | 12,971 | 4,291 | -67% | 1 | 1 | 0% | 2,354 | 1,879 | -20% | 0 | 0 | — |
case-16 | pass→pass | 3,995 | 2,385 | -40% | 1 | 1 | 0% | 639 | 1,457 | +128% | 0 | 0 | — |
case-17 | pass→pass | 10,451 | 1,509 | -86% | 1 | 1 | 0% | 1,774 | 1,220 | -31% | 0 | 0 | — |
case-18 | pass→pass | 10,590 | 5,531 | -48% | 1 | 1 | 0% | 1,808 | 1,916 | +6% | 0 | 0 | — |
case-19 | pass→pass | 12,941 | 2,138 | -83% | 1 | 1 | 0% | 2,175 | 1,377 | -37% | 0 | 0 | — |
case-20 | fail→pass | 9,809 | 1,605 | -84% | 1 | 1 | 0% | 1,832 | 1,281 | -30% | 0 | 0 | — |
case-21 | pass→fail | 13,013 | 5,124 | -61% | 1 | 1 | 0% | 2,554 | 1,914 | -25% | 0 | 0 | — |
case-22 | fail→pass | 9,473 | 4,148 | -56% | 1 | 1 | 0% | 1,769 | 1,820 | +3% | 0 | 0 | — |
case-23 | pass→pass | 4,856 | 1,540 | -68% | 1 | 1 | 0% | 790 | 1,202 | +52% | 0 | 0 | — |
case-24 | fail→pass | 5,050 | 2,238 | -56% | 1 | 1 | 0% | 816 | 1,386 | +70% | 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. 24 cases were attempted, and 20 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 -4 percentage points is the difference between those two pass rates over the 20 comparable cases. 4 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.