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name: memento-skill description: Use this skill to turn fragmented experiments, evidence, notes, and evolving hypotheses into a controlled external memory system. It helps agents separate factual ledgers from belief updates, keep only decision-relevant evidence on the hot path, recall archives only with explicit triggers, and choose the next high-information experiment or contrast. metadata: author: waterdrop26651 license: MIT version: 1.0.0 categories: - research - agent-memory - experiment-tracking tags: - agent-skills - skill-md - codex - claude-code - external-memory - ai-memory - hypotheses - experiments

Memento Skill

Overview

Memento Skill treats research memory as a controlled recall problem. External records help an agent continue work across sessions, but they can also pollute the next decision if stale notes, weak evidence, or misleading summaries are read as current facts.

The goal is not to remember everything. The goal is to keep the current decision surface clear: facts in one ledger, contrasts in another, beliefs in a third, and stale fragments behind an archive index until there is a real reason to recall them.

Default stance: optimize for fast continuation, not exhaustive replay. A good tracker lets a fresh agent recover the current decision surface quickly without re-reading the whole experimental past.

Use this skill when

• The user wants an experiment tracker, ablation plan, run table, or research log.
• The project has many runs and needs a global landscape rather than ad hoc notes.
• The user wants help picking the next experiment by information gain, not intuition alone.
• The user wants to compare predictions against outcomes and update beliefs systematically.
• The user wants to prevent old context, archived findings, or memory fragments from becoming accidental instructions.

Core rules

• Maintain both views at once: the global landscape and the next local gradient.
• Separate active decision support from archival completeness.
• Add a contrast before launching a run.
• Change one primary axis per contrast; keep the rest controlled enough to interpret.
• Prefer experiments whose outcome would most change the roadmap.
• Negative results are useful if the prediction was explicit and the control was clean.
• The worst result is a bad contrast that teaches nothing.
• If an old experiment no longer changes the next decision, demote it out of the default working set.
• Do not let every note become a tattoo: a fragment must be classified, checked, and placed in the right memory layer before it drives action.

Required working set

• runs.csv: factual ledger, one row per run.
• contrasts.csv: reasoning ledger, one row per controlled comparison.
• hypotheses.md: belief ledger, one section per claim and update rule.

If these files do not exist, create them. Use the starter schemas in references/TEMPLATES.md.

Preferred layered layout

For medium or large research programs, prefer this layout:

• CURRENT_STATE.md: the minimum entry point for a fresh agent. Keep it short.
• ACTIVE_TRACKER.md or ACTIVE_TRACKER.csv: only experiments that still have decision gradient for the current roadmap.
• EVIDENCE_LOG.md: compressed statement of what the project currently believes and which few experiments justify that belief.
• runs.csv, contrasts.csv, hypotheses.md: the full ledger.
• archive/: experiments and tracker snapshots that should not be read by default.
• optional ARCHIVE_INDEX.md: a searchable index for cold-memory recall.
• optional archive cards: one short note per archived cluster explaining when it should ever be revisited.
• optional RECALL_NOTES/: small audit trail for archive recalls that actually happened.

Think of these as memory layers:

• hot path: CURRENT_STATE.md, ACTIVE_TRACKER.*, EVIDENCE_LOG.md
• full ledger: runs.csv, contrasts.csv, hypotheses.md
• cold memory: archive/, ARCHIVE_INDEX.md, archive cards, RECALL_NOTES/

Compatibility rule:

• Do not break projects that only have runs.csv, contrasts.csv, hypotheses.md.
• When the tracker has become too large, introduce the layered files above and make them the default reading path.
• Do not require archive files for projects that do not need cold-memory recall yet.

Archive recall model

Treat archive as a cold-memory layer, not as dead storage and not as part of the default read path.

Rules:

• Do not read archive by default.
• Only recall archive when the current problem justifies it.
• Recall should begin from an index or archive card, not from blind directory traversal.
• Recall is for extracting reusable assets, not for replaying the entire old branch.
• If recall finds a still-useful result, promote that result back into the hot path (CURRENT_STATE.md, ACTIVE_TRACKER.*, EVIDENCE_LOG.md) rather than leaving it buried.

Recommended recall triggers:

• current mainline has stalled or failed repeatedly
• current question is structurally similar to an archived question
• a new result conflicts with the active project story
• a high-value anomaly appears and active evidence does not explain it
• the user explicitly asks for historical excavation

Recommended recall budget:

• first read ARCHIVE_INDEX.md or the relevant archive card
• select at most 1-2 archive clusters
• read at most 1-3 source files per cluster before deciding whether to stop
• write a short recall note if the recall materially informs the next step

Workflow

1. Read the smallest authoritative entry point first: prefer CURRENT_STATE.md, then ACTIVE_TRACKER.*, then EVIDENCE_LOG.md, then the full ledger only as needed.
2. Identify the active questions, hypotheses, and unresolved decision points.
3. Mark old experiments as one of:
   • active: still changes current decisions
   • reference: useful supporting evidence but not part of the default working set
   • archived: historical only unless a specific question reopens
4. Propose candidate contrasts and rank them by expected information gain.
5. Write the prediction before execution: direction, minimum meaningful delta, and reason.
6. Record observed facts in runs.csv, not interpretation.
7. Update contrasts.csv with actual deltas, information gain, and next action.
8. Update hypotheses.md based on the gap between expected and observed results.
9. If archive recall is triggered, consult ARCHIVE_INDEX.md or archive cards, do a bounded read, and summarize reusable findings in a recall note.
10. Promote any recalled high-value finding into the hot path instead of keeping it archive-only.
11. Refresh CURRENT_STATE.md and ACTIVE_TRACKER.* so a new agent can resume without replaying the whole ledger.
12. If tracker files exist, run python <path-to-this-skill>/scripts/validate_tracker.py <tracker_dir> after editing them. Resolve the script path relative to this SKILL.md.

Deliverables

When using this skill, produce:

• A concise landscape summary
• The most informative next contrasts, ranked
• The control requirements for each contrast
• The predicted gradient or decision impact for each contrast
• Explicit hypothesis updates after new evidence
• A statement of what moved into the active set, what stayed as reference, and what was archived out of the default reading path
• When archive recall happened: why recall was triggered, what was read, what reusable asset was found, and whether it changed the plan

Quality bar

• runs.csv should store facts and quality flags, not conclusions.
• contrasts.csv should make it obvious what changed, what stayed controlled, what was predicted, and what was learned.
• hypotheses.md should state what would change the belief, not just the belief itself.
• If a contrast is weakly controlled, mark it inconclusive instead of pretending it resolved the question.
• CURRENT_STATE.md should be readable in one short sitting.
• ACTIVE_TRACKER.* should contain only the experiments with nontrivial current decision gradient.
• Historical completeness belongs in the ledger and archive, not in the default agent entry point.
• Archive should be indexed by problem value, not only by date or snapshot name.
• Useful archived findings should be promotable back into the hot path with minimal rewriting.

See references/REFERENCE.md for the full rubric and references/TEMPLATES.md for starter files and examples.