| name | reference-intelligence |
| description | 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. |
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:
references:
mode: none | curated | live | both
why: <short reason>
curated: <relevant Agentit index/playbook entries, if any>
authority_needed: <canonical / licensed artifact / corroborated / inspiration / mixed>
provenance: <whether a durable project reference record is useful>
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:
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:
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:
- identify recurring jobs;
- group near-duplicates;
- extract required inputs;
- extract useful intermediate artifacts/decisions;
- extract QA/evidence expectations;
- turn genuinely reusable gaps into the existing domain skill/reference;
- preserve source provenance;
- 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:
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:
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:
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:
