The operator+agent discipline for draining a client's corpus inbox (clients/<client>/corpus/inbox/, 162 pending in reach-edu as of 2026-07-25) — agent scans each pending file, stamps a triage_suggestion, and proposes destinations in confidence-banded batches; the operator sweeps. Use whenever the user says "triage the inbox", "work through the inbox", "drain the inbox", "/triage-inbox", or asks to file/sort pending inbox captures. Encodes the destination model (funder org folder, person, or domain — with SurrealDB as the single canonical index and cheap reference .md copies fanned into any additionally-relevant folder), the six task lanes (TRIAGE/EXTRACT/ENRICH/DEDUPE/FLAG/DISCARD), the tagging convention (YAML array, Train-Case with lowercase connector words), and the batch/resume rhythm. Co-pilot phase: agent proposes, operator disposes; nothing files without an operator sweep.

2026-07-251

inbox-curation

lossless-group/augment-it

didi's triage discipline for filing captured links and sources into the right corpus (thesis/strategy/topic) inside augment-it. Use whenever the user gives didi a URL to save, asks to "file this under <corpus name>", names an existing thesis/strategy by name for a link to go under, or it's unclear which corpus a captured link belongs to. Encodes the decision tree (named + existing corpus → source.add directly; new corpus → propose domain.create first; unclear → propose or park in the inbox), the domain-resolution discipline (resolve names against the live "Existing corpora" list, never fabricate a domain_type/domain_slug), and the boundary with corpus.inbox.add (the untriaged capture-first fallback). The capability catalog and args shapes live in services/workspace/src/chat.ts's CURATOR_CHAT_VERBS slab — this skill is the reasoning on top of it.

2026-07-081

name

decile-hub-interface

description

The operator-facing workflow layer for getting real-world data INTO and OUT OF a client's Decile Hub CRM — sitting on top of the decile-hub-connector API contract. Use whenever importing a batch of real records into Decile (voice-note transcripts, CSV lead lists, LP rosters, event attendees), matching hand-collected records to existing Decile prospects by name, appending notes to people/prospects, or reasoning about the People-vs-Organizations-vs-Pipelines-vs-Prospects data model for a fundraising pipeline. Triggers when the user says "import these into Decile", "add these to the pipeline", "match these to Decile", "put these voice notes / this list into Decile", "attach a note to the prospect", "which pipeline do these belong to", or names the importer script decile-import-voicenotes.mjs. Encodes the read-only introspection recipe (whoami → pipelines?kind=investor → stages → prospect shape), the name-matching import pattern (Apple Voice Memos and most hand-collected records carry NO email, so the filename/typed-name is the identity key and Decile supplies the rest), where notes actually land (the Person/prospectable, NOT the pipeline row — so they surface across every pipeline that Person is in, tied to one pipeline only by the note's context label), the idempotent + additive + dry-run-first import discipline, the aliases.json spelling-bridge, and the .env gotchas that silently break the connector. The authoritative API contract is [[decile-hub-connector]] + the on-disk swagger; this skill is how a human operator drives it.

Decile Hub Interface

The operating patterns for moving real-world data into and out of a client's Decile Hub CRM. Where [[decile-hub-connector]] documents the API (auth, the three pagination patterns, endpoint inventory, upsert semantics), this skill documents the workflow an operator + agent run together: introspect the tenant, find the right pipeline, match hand-collected records to existing prospects by name, and append notes — safely, idempotently, and with the human in the driver's seat.

Two layers, one system. If you need a field name, an endpoint, or a pagination envelope, read [[decile-hub-connector]] and the swagger at clients/humain-vc/inputs/decilehub/202506_decilehub-docs_swagger.yaml. If you need to know how we actually push a pile of notes or leads in without making a mess, you're in the right place.

The data model, in the order you reason about it

Decile is three layers, not two. Getting this right answers the recurring "is this a person or an organization?" question:

People — individuals. Natural key email.
Organizations — firms / funds / family offices. Natural key name.
Pipelines → PipelineProspects → Notes — the relationship layer on top of People & Orgs.
- A Pipeline has a kind. kind=investor is the LP-fundraising type.
- A PipelineProspect is a row in a pipeline pointing at exactly one of a Person or an Organization (prospectable_type / prospectable_id). For individual LP leads the prospect is the Person; their fund/family-office rides along as person.organizations[], never as the prospect itself. You only make the Organization the prospect when the institution — not a named human — is the relationship.
- The prospect carries the pipeline-scoped facts: stage ({id, name}), rating, probability, capital_commitment, last_contact, next_contact, assigned_name.
- Notes are their own entity ({id, body, context, created_at, author}).

Where a note actually lands (important, and counter-intuitive)

POST /api/v1/pipeline_prospects/{id}/notes does not attach the note to the pipeline row. Per the spec, "the note is stored against the prospect's prospectable (Person / Organization) when one exists, falling back to the prospect itself otherwise." Since LP prospects are prospectable_type: "Person", the note attaches to the Person.

Consequence: a note added via the Fund I prospect shows on that person's card in every pipeline they belong to (e.g. the same LP who is also in a CogScAI-SPV pipeline). The note's context field — which we set to the pipeline name — is the only thing tying it to one pipeline. That's usually what you want (whole-relationship-at-a-glance). If you truly need per-pipeline siloed notes, Decile's model doesn't do that — encode the pipeline into the note body instead.

Step 1 — read-only introspection (always run first, mutates nothing)

Never write blind. Ground every import in the live tenant:

GET /api/v1/whoami                       # token works? kind? accessible_pipeline_ids?
GET /api/v1/pipelines?kind=investor      # the investor pipelines + which is is_primary
GET /api/v1/pipelines/{id}               # the REAL stage names + ids (don't guess)
GET /api/v1/pipeline_prospects?pipeline_id={id}&page=0   # prospect shape; paginate (Pattern A)

A prospect row looks like: { id, name, first_name, last_name, email, prospectable_type, prospectable_id, stage:{id,name}, rating, probability, capital_commitment, notes:[{body,…}], … } — note that notes[] comes back inline on the list response, so you can dedup against existing notes without an extra GET.

A tenant can have several investor pipelines all flagged is_primary: true (humain-vc has three: a Fund I pipeline and two SPV pipelines). Don't assume one. Pick the pipeline whose stages match the vocabulary in your source data — e.g. voice notes that say "Declined" / "Future Interest" belong to the pipeline that actually has those stages.

Step 2 — match hand-collected records to prospects BY NAME

Most real inputs (Apple Voice Memos, scraped lists, business cards) carry no email. So:

The typed name is the identity key. The operator typed it deliberately — trust it, but it may not match Decile's spelling.
Decile supplies the rest. Once you match by name you recover the email, stage, and prospect id from the prospect record.
Normalize before comparing: NFKD → strip diacritics → lowercase → collapse non-alphanumerics to single spaces. (So Coutiño == Coutino, HO Maycotte == ho maycotte.)
Three match outcomes to report, not silently resolve:
- exact (unique normalized full-name hit) → queue it
- last-name-only (unique surname, first name differs) → suggest, never auto-apply
- none / multiple → report for the human to resolve
Spelling mismatches are the norm, not the exception. In the 2026-06-11 humain-vc import, 6 of 35 LPs were spelled differently in Decile (Danby→Dabby, Shetty→Shetti, Schlecht→Schlect, Du Monceux→Dumonceaux, …). Two resolution paths — pick per your correction's permanence:
- Fix the source spelling (rename the file + fix the body) when the operator confirms the canonical name. Preferred — the source becomes correct.
- aliases.json in the input dir ({ "Typed Name": "Decile Name" }) when you want to keep the source as-is. The importer redirects the lookup name but keeps the idempotency marker keyed to the source filename, so it stays safe to re-run.

Step 3 — import with the reusable script

scripts/decile-import-voicenotes.mjs (in the augment-it parent repo) is the worked reference implementation of this whole pattern:

node scripts/decile-import-voicenotes.mjs                 # DRY RUN (default) — mutates nothing
node scripts/decile-import-voicenotes.mjs --apply         # append the matched notes
node scripts/decile-import-voicenotes.mjs --pipeline "Fund I" --client humain-vc \
     --dir clients/humain-vc/inputs/<batch> --alias "Typed Name=Decile Name"

Design invariants worth copying into any new importer:

Dry-run by default. --apply is the only thing that writes. The dry run is the match report the operator signs off on.
Idempotent. Every note body is prefixed with a stable marker [voice-note:<date>:<slug>]; a prospect already carrying that marker is skipped. Re-running never duplicates.
Additive, never destructive. It only appends notes. It never edits existing notes and — deliberately — never moves stages. Stage is report-only; stage changes are a human call.
Per-client env, resolved from clients/<slug>/.env (falls back across the legacy alias var names). Token sent raw in Authorization (no Bearer).

Operator discipline (why this skill exists)

Web/voice/scraped data isn't accurate enough to auto-trust, and CRM writes are semi-permanent:

Human-in-the-driver's-seat. Show the match report; let the operator confirm spellings and approve the write. Don't auto-resolve ambiguous or missing matches.
Additive enrichment never overrides. Notes accumulate; they don't clobber operator-curated fields or existing notes. Re-runs surface only what's new.
Preserve the raw before you clean. If you rewrite transcripts (grammar cleanup), commit the raw first so the edit is a reviewable, recoverable diff.
Faithfulness over polish when cleaning source text: fix grammar and obvious mis-hears, but never invent facts, names, numbers, or stage words.

Gotchas that silently break things

DECILE_API_URL must be the tenant ROOT (https://<tenant>.decilehub.com), not the docs page. humain-vc's .env was set to .../docs/api and every call 404'd. Routes live under /api/v1/.
Var names are DECILE_API_URL + DECILE_HUB_API_KEY (what services/decile-mcp/server.ts reads). Older .envs used DECILE_API_BASE_URL / DECILE_API_KEY; if the code reads the new names and the .env only has the old ones, it silently gets empty strings.
Notes attach at the Person level (see above) — don't promise a user that a note is scoped to one pipeline; it isn't.
is_primary is not unique — several investor pipelines can all be primary. Match on stage vocabulary, not the flag.

decile-hub-interface

Decile Hub Interface

The data model, in the order you reason about it

Where a note actually lands (important, and counter-intuitive)

Step 1 — read-only introspection (always run first, mutates nothing)

Step 2 — match hand-collected records to prospects BY NAME

Step 3 — import with the reusable script

Operator discipline (why this skill exists)

Gotchas that silently break things

See also

Decile Hub Interface

The data model, in the order you reason about it

Where a note actually lands (important, and counter-intuitive)

Step 1 — read-only introspection (always run first, mutates nothing)

Step 2 — match hand-collected records to prospects BY NAME

Step 3 — import with the reusable script

Operator discipline (why this skill exists)

Gotchas that silently break things

See also