Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Export a research session into knowledge-intake compatible markdown. Use after a research session to hand findings to memory-palace.
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See how to installReview the instructions the agent will receive before you install.
Turn a finished research session into a file knowledge-intake can read
without reformatting.
After a research session has findings and you want them stored rather than merely reported.
Skill(tome:research)
first, since an export of an empty session emits only frontmatter.Skill(tome:synthesize), which
produces the narrative form with coverage and gaps.Load the session and render it:
from pathlib import Path
from tome.session import SessionManager
from tome.output.export import export_for_memory_palace
session = SessionManager(Path.cwd()).load_latest()
markdown = export_for_memory_palace(session)
Path(f"docs/research/export-{session.id}.md").write_text(markdown)
SessionManager resolves its store relative to the path it is given, so
run this from the repository root or the session will not be found.
To export a specific session, use load(session_id) instead of
load_latest().
YAML frontmatter carrying topic, domain, session_id, date,
finding_count, channels, and type: research-export, followed by
findings grouped by channel. Each finding renders its source, URL,
relevance, summary, and channel-specific metadata.
An empty session emits the frontmatter and a line stating that no findings were recorded, which is deliberate. A silent empty file would read as a successful export of nothing.
The output is an external resource, not a corpus entry. Feed it to
Skill(memory-palace:knowledge-intake), which owns evaluation, routing,
and storage. This skill does no scoring.
export_for_memory_palace was called on a loaded sessiontype: research-exportfinding_count in the frontmatter matches the session's findingsSkill(memory-palace:knowledge-intake) or
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
The English summary is being prepared. Showing the original description.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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