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lockedin-capture

Converts in-session capture intents ("save this", "log this", "track this", "absorb this") into structured vault entries via a two-turn writer/reviewer pattern with duplicate detection and reconciliation. Writer proposes entity/edge structure; reviewer scores against five rubric dimensions and surfaces duplicate candidates for user decision. Activate when the user signals explicit capture intent f

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Atualizado há 5 meses

Ver no GitHub ↗ Licença: MIT

Como adicionar

/plugin marketplace add daypunk/LockedIn

O comando exato pode variar conforme o repositório. Confira o README no GitHub.

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Cole no README do seu repo

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Ler descrição completa

lockedin-capture

Dedicated capture skill for in-session work moments. Promotes capture from an implicit sub-role of the main lockedin skill into a first-class calibrated flow with its own writer/reviewer contract, matching the rigor of lockedin-render-jaso and lockedin-render-resume-en.

Capture quality determines vault quality. Vault quality determines render quality. This skill owns that first link.

Use this skill when

  • The user says "save this", "log this", "track this", "absorb this",

"remember this", "capture this", or "add this to my vault".

  • The user describes a thing they just did: shipped a feature,

attended a meeting, made a decision, learned something, completed a project milestone.

  • The user pastes notes, a git log excerpt, a PR description, a Slack

summary, or any short text and says to save it.

  • The main lockedin skill detects capture intent during a session

and routes here.

Do NOT use this skill when

  • The user is doing coding, debugging, or writing without any signal

that they want to persist the work moment to the vault.

  • The user asks to render an artifact (resume, jaso, ideas) — route to

the appropriate render skill instead.

  • The user wants to query their vault without adding anything — use the

main lockedin skill's query flow.

  • The user wants to audit a document — use lockedin-audit.

Execution model

User input
    │
    ▼
1. Writer turn (prompt-writer.md)
   - Read user input + any provided context
   - Propose entity types, fields, edge structure
   - Run _infer_edges() mentally from EDGE_SCHEMAS domain/range
   - Quote source phrases from user input
   - Output: structured proposal (markdown table)
    │
    ▼
2. ValidatorDeterm (lockedin validate --dry-run)
   - Deterministic field-type and required-field check
   - Catches schema errors before LLM reviewer sees the proposal
    │
    ▼
3. Reviewer turn (prompt-reviewer.md) — SEPARATE Claude context
   - Re-load RUBRIC.md fresh (not from writer-turn memory)
   - Score 5 dimensions independently
   - Query vault for duplicate candidates (slug similarity, alias match)
   - Output: JSON score + candidates + suggested revisions
    │
    ▼
4. ReconcileNegotiator (if duplicates surfaced)
   - Present candidates to user as a numbered list
   - Ask one question: merge / update / create new
   - Never auto-merge, never auto-create-duplicate
    │
    ▼
5. write-before-confirm
   - State what will be written: "Saving 2 entries — meeting X, decision Y. OK?"
   - On confirmation: call write_entity for each proposed entity
   - Edges written atomically with entities

The write-before-confirm step applies even if the user has granted write permission earlier in the session. No silent vault mutations.

Rubric dimensions

Full definitions and score bands in RUBRIC.md. Summary:

  1. Schema conformance — required fields populated correctly.
  2. Edge completeness — all inferable edges from EDGE_SCHEMAS present.
  3. Field specificity — values carry concrete information; no placeholders.
  4. Semantic accuracy — entity types match the user's stated intent.
  5. Duplicate detection + reconciliation — candidates surfaced and

resolved with user; never silent-merge or silent-create-duplicate.

Pass criterion: every dimension >= 4, no silent duplicate creation.

Reconciliation policy

  • Never silent-merge: do not fold a new entity into an existing one

without presenting the match and asking the user.

  • Never silent-create-duplicate: do not write a new entity when a

candidate with matching slug, name, or aliases already exists in the vault without asking the user first.

  • Surface candidates: when the vault has a likely duplicate

(slug-distance <= 2, same type, matching name or alias), present it as: "I found a possible match: [[type/slug]] — same project? Merge into it, update it, or create a separate entry?"

  • One question, one candidate at a time: if multiple duplicates

exist, handle them sequentially.

Output

After the writer/reviewer cycle and user confirmation:

  • Confirmed vault entries, one markdown file per entity under

<vault>/experience/<type>/<slug>.md.

  • Edges encoded in each entity file's links: frontmatter block.
  • EXPERIENCE.md regenerated at the vault root (auto via

write_entity).

  • Reviewer JSON score stored at

<vault>/metrics/capture/<iso-timestamp>.json for drift tracking.

Files in this directory

SKILL.md            (this file)
RUBRIC.md           5 dimensions; score bands; fixture authoring guide
prompt-writer.md    writer-turn instruction
prompt-reviewer.md  reviewer-turn instruction (separate Claude context)
AGENTS.md           four agents: Writer, ValidatorDeterm, Reviewer, ReconcileNegotiator
TOOLS.md            deterministic tools the skill uses
banned_phrases.json phrases that indicate low-quality field values
research-notes.md   sources informing capture design rationale
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