# Reference Intelligence > Decide whether a task materially benefits from external references, then use the smallest domain-appropriate curated/live set. Distinguish authority, actually read material sources, distill useful knowledge into existing skills, and record durable provenance without making the user re-paste references. - Skill: `marcmarti9/reference-intelligence` (Agent Skill) - Install (CLI): `npx skillmds@latest add marcmarti9/reference-intelligence` - Raw SKILL.md: https://api.skillmd.com/api/skills/marcmarti9/reference-intelligence/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: marcmarti9 (https://skillmd.com/u/marcmarti9) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/marcmarti9/reference-intelligence --- # Reference Intelligence The purpose of this skill is simple: > **Always decide whether references would materially improve the task; do not always load references.** The primary AI owns that judgment. Agentit must not recreate it with keyword classifiers, scoring code, or a bespoke reference router. ## 1. Decide the reference mode For every material `TASK_DECISION`, choose one: ```text references: mode: none | curated | live | both why: curated: authority_needed: provenance: ``` ### `none` Correct when outside material knowledge would not change the result. Examples: - rename a local private function; - formatting/cleanup; - self-contained bug fully explained by repository evidence; - small mechanical edit. Do not research for ceremony. ### `curated` Use when Agentit already contains a useful recurring playbook/reference. Start at `references/INDEX.md`, then read the linked domain skill/reference file that matters. Examples: - premium public website -> design reference index + `premium-web-production.md`; - substantial marketing work -> `marketing-operating-system.md`. ### `live` Use when the answer depends on current or domain-specific authority not pre-curated in Agentit. Examples: - Spanish fiscal report -> current legislation / tax authority / authoritative fiscal sources; - current framework/API integration -> current official docs; - current legal/security/platform requirements -> current authoritative sources. Agentit does **not** need a prebuilt pack for every possible human domain. The model can research the correct sources when the task requires them. ### `both` Use when a curated procedure improves how to work while live sources establish current facts. Example: ```text SEO task curated -> how to structure the audit/feedback loop live -> Search Console data, current SERP/platform guidance, current site evidence ``` ## 2. Prefer underlying material over the social post A bookmark often points to the real asset: - article/thread; - repository; - course; - prompt collection; - launch library; - component catalog; - official product docs. When the underlying material contains substantially more useful knowledge, **read it**. Do not stop at the tweet summary. Then decide where the durable knowledge belongs: ```text one-off fact -> use/cite in current task recurring procedure -> existing skill or its references/*.md reusable licensed artifact -> evaluate adopt/adapt/compose reference library/tool -> index as a discovery source current domain truth -> research live when needed hype / unrelated curiosity -> do not promote into Agentit core ``` This is why the bookmarked premium-web articles and large marketing prompt corpus are distilled inside the relevant design/marketing skills rather than existing only as URLs in a catalog. ## 3. Distinguish source roles A source's role determines what can be inferred from it. - **canonical** — official docs/specification/source of truth for the thing being used; - **licensed artifact** — reusable code/skill/assets whose license has been inspected; - **corroborated evidence** — factual claim supported independently; - **creator/vendor claim** — useful lead but not independent proof; - **inspiration** — pattern/taste/process input, not factual authority; - **internal evidence** — project/client data that establishes what happened in this system. Examples: - a beautiful site can influence composition but does not prove conversion; - a vendor article can reveal a useful agent architecture but its ROAS claim remains a vendor claim; - an MIT skill can be selectively adapted with attribution; - current official tax guidance can establish a fiscal rule, while a design bookmark obviously cannot. ## 4. Distill, do not hoard The valuable Agentit unit is normally **a capability/procedure**, not the original prompt wording. When a source contains many prompts/examples: 1. identify recurring jobs; 2. group near-duplicates; 3. extract required inputs; 4. extract useful intermediate artifacts/decisions; 5. extract QA/evidence expectations; 6. turn genuinely reusable gaps into the existing domain skill/reference; 7. preserve source provenance; 8. discard redundant wording and hype. Do not put 500 prompts into every marketing context. Teach the agent the marketing operating system behind them. Likewise, a “$10k website” article should become a production playbook, not the instruction “make this look expensive”. ## 5. Curated index is intentionally small `references/INDEX.md` is an agent-readable discovery map. It is not a database and has no custom Python runtime. Keep something there only when it is broadly useful enough to accelerate future tasks. Do **not** globally carry interesting-but-specialized bookmarks merely because they exist. If a future task specifically concerns an omitted topic, research it live then. Default preference: > enrich an existing skill with a deep `references/*.md` file instead of creating another top-level system. ## 6. References must actually affect the work A reference is not “used” because its name appears in a plan. For each material reference, the working agent should be able to state compactly: ```text source: role: what I actually learned/observed: what decision it changed: what I deliberately did not infer/copy: ``` If it changed nothing, it normally does not belong in project provenance. ## 7. Durable project provenance When external knowledge materially changes architecture, product behavior, visual direction, process, dependency selection, or another expensive-to-rediscover decision, update the project's existing canonical reference/decision documentation. If there is no equivalent, `docs/agentit/REFERENCES.md` is the default lightweight ledger. Record: ```text source -> role -> extracted principle/evidence -> project decision -> affected area -> verified date ``` Do not turn the ledger into browsing history, a transcript, or chain-of-thought. For reused code/skills/assets, preserve required license/attribution in the appropriate project notice as well. ## 8. Verification uses Agentit's existing runtime Reference Intelligence does **not** need a second bespoke Python verifier. When references are material to acceptance, bind the evidence into the existing Loop/Graph contract and verifier, for example: ```text goal: implement the approved public landing-page direction verifier includes: - required current docs were inspected where implementation depends on them - premium-web reference playbook/design research was actually used - rendered desktop/mobile result passes project checks - material external influences are recorded in project provenance ``` The existing runtime enforces the verifier/receipt. The model decides **what evidence matters**; software mechanically enforces the chosen acceptance contract. ## 9. Auditor responsibilities The independent decision auditor should challenge: - `none` when current/domain knowledge obviously matters; - loading unrelated references; - relying on model memory for dynamic/high-stakes facts; - stopping at a tweet when its linked article/repo contains the real substance; - creator/vendor claims promoted to facts; - 500-prompt/context dumps instead of distilled procedures; - design cloning; - dependency/artifact reuse without license/fit review; - claiming a reference was used when it produced no traceable decision/evidence. The auditor challenges; it does not become the semantic router. ## Completion check For substantial reference-driven work: - [ ] reference mode was chosen contextually; - [ ] only relevant sources were loaded; - [ ] underlying material was inspected when it contained the useful substance; - [ ] current authoritative sources were used when required; - [ ] reusable knowledge was distilled into the appropriate domain skill/reference rather than duplicated blindly; - [ ] source roles/claims were not confused; - [ ] durable material influences were documented where useful; - [ ] the actual result was verified independently of the references.