research-log

name	research-log
description	Record, manage, and query research experiment logs. Use when the user wants to log an experiment result, amend an existing entry, view recent logs, rebuild the index, or plan, execute, repeat, or diagnose research in a project containing docs/research_log/. Triggers on phrases like "log this experiment", "record results", "add a log entry", "show recent experiments", "amend log", "try this method", "change these experiment parameters", and "why did this run fail?". Each entry creates a structured Markdown file in docs/research_log/. Suggest running this before /report-slides when new results have not been logged yet.
metadata	{"data_access_level":"raw","task_type":"open-ended"}

Research Log

Manages a structured experiment journal as individual Markdown files. One file per experiment; INDEX.md is a derived view rebuilt on demand.

Storage

Resolve the log directory first (see skills/resource-resolver/SKILL.md):

# macOS / Linux / Git Bash:
RESOLVE="$(find ~/.claude -path "*/resource-resolver/scripts/resolve.py" | head -1)"
ROLE_JSON=$(python "$RESOLVE" --role research_log --json)
RESEARCH_LOG_DIR=$(echo "$ROLE_JSON" | python3 -c "
import json, sys
d = json.load(sys.stdin)
if d.get('status') == 'resolved':
    print(d['primary'])
")

# Windows (PowerShell):
$RESOLVE = (Get-ChildItem $env:USERPROFILE\.claude -Recurse -Filter resolve.py |
    Where-Object FullName -like "*resource-resolver*" | Select-Object -First 1).FullName
$RoleJson = python $RESOLVE --role research_log --json | ConvertFrom-Json
$RESEARCH_LOG_DIR = if ($RoleJson.status -eq "resolved") { $RoleJson.primary } else { "" }

Only a "resolved" status yields a usable path — a stale_mapping response also carries a non-empty primary, so never read primary without checking status. If $RESEARCH_LOG_DIR comes back empty, inspect $ROLE_JSON and follow the branching rules in the "Calling convention for other skills" section of skills/resource-resolver/SKILL.md: an error key means surface message and stop; status: "stale_mapping" means the previously configured log directory is gone and the user must re-confirm it (do not silently recreate it at the old path); only "unresolved" / "no_candidates" leads to the normal first-use confirmation flow (--set research_log --path <path> [--create]).

The rest of this file refers to the resolved directory as $RESEARCH_LOG_DIR.

Shell state does not persist across separate tool-call invocations. Nearly every section below (add, amend, index, query, milestones) runs its own bash/PowerShell call, and $RESEARCH_LOG_DIR will not still be set in those later calls. Either re-run the resolve snippet above in that same call, or substitute the already-resolved path as a literal value into the command — never run a command that would expand $RESEARCH_LOG_DIR to an empty string.

Filename: YYYY-MM-DD_<experiment-slug>.md Index: $RESEARCH_LOG_DIR/INDEX.md (auto-generated, never hand-edited) Milestones: $RESEARCH_LOG_DIR/MILESTONES.md (auto-generated once milestone mode is active — see Milestone Mode below; never hand-edited)

---

Milestone Mode

For journals with many entries, a lightweight grouping layer keeps lookups token-efficient: instead of reading the full flat INDEX.md, an agent reads a small MILESTONES.md first, picks the relevant milestone from its description, then opens only the log file(s) that matter.

Auto-enable, one-directional. Milestone mode turns on by itself once total log content crosses a token threshold — there is no command to turn it on manually. Once $RESEARCH_LOG_DIR/MILESTONES.md exists, always keep maintaining it; never delete it or stop updating it, even if log volume later drops (e.g. old entries removed). There is no "disable" flow.

Checking the threshold (run this as part of index, and again before finalizing any add):

# macOS / Linux / Git Bash:
LOG_STATS="$(find ~/.claude -path "*/research-log/scripts/log_stats.py" | head -1)"
python "$LOG_STATS" --dir "$RESEARCH_LOG_DIR" --json

# Windows (PowerShell):
$LOG_STATS = (Get-ChildItem $env:USERPROFILE\.claude -Recurse -Filter log_stats.py |
    Where-Object FullName -like "*research-log*" | Select-Object -First 1).FullName
python $LOG_STATS --dir $RESEARCH_LOG_DIR --json

The script prints a JSON object: file_count, total_chars, estimated_tokens, milestones_exists, threshold (default 6000, override with --threshold), and recommend_enable — true only when milestones_exists is false and estimated_tokens >= threshold.

When recommend_enable is true (first trigger — the check itself runs automatically; no permission prompt is needed to start this flow):

1. Scan all existing logs. For any missing a summary: field, draft one from its Goal/Conclusion sections and batch them for the user to confirm or edit.
2. Cluster logs into candidate milestones using two signals: tag overlap (Jaccard similarity between each log's tags) and date gaps between consecutive logs. Draft a short title and one-paragraph description per cluster.
3. Present the proposed grouping to the user. Let them confirm, rename, merge, or split clusters — never finalize a grouping silently.
4. Write $RESEARCH_LOG_DIR/MILESTONES.md (format below) and add a milestone: field (e.g. M2) to each affected log's frontmatter.

MILESTONES.md format (newest milestone first):

# Research Milestones

_Last updated: YYYY-MM-DD_

## M2: <short title>
<one-paragraph description>
**Date range:** YYYY-MM-DD – YYYY-MM-DD · **Status:** ongoing|closed

| Date | Experiment | Summary |
|------|-----------|---------|
| YYYY-MM-DD | <slug> | <that log's `summary:` frontmatter value> |

## M1: <short title>
...

Ongoing use, once MILESTONES.md exists:

• Reading: read MILESTONES.md first, pick the relevant milestone from its description and table, then open only the specific log file(s) needed — don't fall back to reading all of INDEX.md unless no milestone matches.
• Writing (add): compare the new entry's tags against the most recent milestone's aggregate tags (Jaccard overlap) and the date gap since that milestone's last entry. If overlap is low or the gap is large, suggest starting a new milestone with a drafted title/description; otherwise suggest continuing the current one. The user confirms or overrides; if the user gives no clear signal, default to continuing the current milestone. Update the log's milestone: field and refresh MILESTONES.md.

INDEX.md's format is unchanged by milestone mode — it remains the complete flat chronological reference regardless of whether milestones are active.

---

Commands

add

Create a new log entry.

Step 0 — Gather git context (run silently before asking questions)

Find the most recent prior log, then run git_context.py to extract recent commit history. Use the output to pre-fill the Changes section and to capture the git_head value.

# macOS / Linux / Git Bash:
GIT_CTX="$(find ~/.claude -path "*/research-log/scripts/git_context.py" | head -1)"
PRIOR=$(ls -t "$RESEARCH_LOG_DIR"/*.md 2>/dev/null | grep -v INDEX | grep -v MILESTONES | head -1)
if [ -n "$PRIOR" ]; then
    python "$GIT_CTX" --since-log "$PRIOR"
else
    python "$GIT_CTX" --since $(date -d "14 days ago" +%Y-%m-%d 2>/dev/null \
                                 || date -v-14d +%Y-%m-%d)
fi
GIT_HEAD=$(python "$GIT_CTX" --head)

# Windows (PowerShell):
$GIT_CTX = (Get-ChildItem $env:USERPROFILE\.claude -Recurse -Filter git_context.py |
    Where-Object FullName -like "*research-log*" | Select-Object -First 1).FullName
$PRIOR = Get-ChildItem $RESEARCH_LOG_DIR -Filter "*.md" |
    Where-Object { $_.Name -ne "INDEX.md" -and $_.Name -ne "MILESTONES.md" } |
    Sort-Object LastWriteTime -Desc |
    Select-Object -First 1 -Exp FullName
if ($PRIOR) { python $GIT_CTX --since-log $PRIOR }
else         { python $GIT_CTX --since (Get-Date).AddDays(-14).ToString("yyyy-MM-dd") }
$GIT_HEAD = python $GIT_CTX --head

If the script prints (not a git repository — skipping git context), continue without git context.

Use the Suggested Changes bullets from the output as the default content for the Changes section. Show them to the user and let them accept, edit, or ignore. Store the Current HEAD value for the git_head frontmatter field.

---

Step 1 — Ask mode: Quick (3 questions, good for in-progress runs) or Full (all sections)?

Quick questions:

1. Experiment name (slug for filename, e.g. run_v2)
2. Goal of this experiment
3. Observations / preliminary results
4. Next steps

Full questions (ask section by section; user may skip any):

1. Experiment name
2. Changes made — show git-suggested bullets as default; user accepts or edits
3. Setup (checkpoint, dataset, key parameters)
4. Results (numbers, tables)
5. Failures / pitfalls
6. Analysis and observations
7. Chart paths (relative paths, one per line)
8. Conclusion
9. Next steps

After answers: ask if this experiment follows a prior one (list existing files to help).

Step 2 — Draft summary (always, regardless of milestone mode): Draft a one-sentence summary: from the Goal (and Conclusion, if present). Show it to the user to accept or edit.

Step 3 — Milestone check (only if $RESEARCH_LOG_DIR/MILESTONES.md already exists): Follow "Ongoing use" under Milestone Mode above: compare this entry's tags/date against the most recent milestone, suggest continuing it or starting a new one, and set the milestone: field to the user's choice.

Write the file:

---
date: YYYY-MM-DD
experiment: <slug>
mode: <exp|daily|explore|report|publish, or omit if unknown>
tags: []
summary: <one-sentence description, always present>
milestone: <id such as M2, or omit if milestone mode is not active>
follows: <prior-filename-or-empty>
reason_follows: <one-line reason or empty>
git_head: <short SHA from git_context.py --head, or empty if not a git repo>
slide_decks: []
amended: []
---

## Goal
<content>

## Changes
<content, or omit if quick mode>

## Setup
<content, or omit>

## Results
<content, or omit>

## Failures
<content, or omit>

## Analysis
<content>

## Charts
<relative ,   ,  >

## Conclusion


## Next Steps

Omit any section that was skipped. Quick mode writes: Goal, Analysis, Next Steps.

Rebuild INDEX.md after saving.

If $RESEARCH_LOG_DIR/MILESTONES.md already exists, also update it: append this entry's row to its milestone's table and refresh _Last updated_. If it does not exist yet, run the Milestone Mode threshold check — if recommend_enable is true, run the enable flow (see Milestone Mode above) before finishing.

Pre-filled add (called from research-mode)

When /research-log add is invoked by research-mode with pre-filled data:

• Skip interactive questions for fields already provided in the pre-filled draft
• Only ask for fields that are empty or marked {{PLACEHOLDER}}
• The mode: field is always set by research-mode; do not ask the user for it
• After writing the file, update slide_decks: in the calling log entry if applicable

Confirm: ✓ Created $RESEARCH_LOG_DIR/YYYY-MM-DD_<slug>.md

---

amend [name-or-date]

Update an existing entry.

If no argument, list the 5 most recent files and ask which to edit:

ls -t "$RESEARCH_LOG_DIR"/*.md | grep -v INDEX | grep -v MILESTONES | head -5

Optional — show git changes since this entry was created:

GIT_CTX="$(find ~/.claude -path "*/research-log/scripts/git_context.py" | head -1)"
python "$GIT_CTX" --since-log <target-log-file>

Useful when the user wants to add new results or fill in a Changes section retroactively.

Show current section headings. Ask which sections to update. Rewrite the file with new content. Append to amended: in frontmatter:

amended:
  - date: YYYY-MM-DD
    summary: <one-line description of change>

If the amendment substantially changes the entry's content, ask (non-blocking — skip if the user declines) whether summary or milestone membership should be revisited.

Rebuild INDEX.md. If $RESEARCH_LOG_DIR/MILESTONES.md exists, rebuild it too.

---

index

Rebuild INDEX.md by scanning all .md files (excluding INDEX.md and MILESTONES.md):

find "$RESEARCH_LOG_DIR" -maxdepth 1 -name "*.md" ! -name "INDEX.md" ! -name "MILESTONES.md" | sort -r

Read frontmatter from each file. Write:

# Research Log Index

_Last updated: YYYY-MM-DD_

| Date | Experiment | Mode | Tags | Follows | HEAD | Slides |
|------|-----------|------|------|---------|------|--------|
| 2024-11-02 | run_v2 | exp | training, ablation | run_v1 | a1b2c3d | ✅ reports/2024-11-05 |
| 2024-10-28 | run_v1 | — | baseline | — | e4f5g6h | ❌ |

Rules: newest first; Mode = mode frontmatter value, or — if absent; HEAD = git_head value or —; Slides = ✅ with deck name if slide_decks non-empty, else ❌; Follows = experiment slug (not full filename), or —.

After rebuilding INDEX.md, run the Milestone Mode threshold check (see above):

• If $RESEARCH_LOG_DIR/MILESTONES.md already exists, rebuild it too, from all logs' milestone: and summary: frontmatter fields.
• If it does not exist and recommend_enable is true, run the enable flow from Milestone Mode above.

---

show [n]

Show the n most recent entries (default 5). For each, print a compact summary: date, experiment, goal excerpt, next steps excerpt, git_head, slide status.

---

query

Query historical sections without modifying the journal. First locate the public CLI; do not assume an installed skill path.

# macOS / Linux / Git Bash:
SECTION_QUERY="$(find ~/.claude -path "*/research-log/scripts/section_query.py" | head -1)"
python "$SECTION_QUERY" types --dir "$RESEARCH_LOG_DIR" --budget 4000

# Windows (PowerShell):
$SECTION_QUERY = (Get-ChildItem $env:USERPROFILE\.claude -Recurse -Filter section_query.py |
    Where-Object FullName -like "*research-log*" | Select-Object -First 1).FullName
python $SECTION_QUERY types --dir $RESEARCH_LOG_DIR --budget 4000

Use the strict types → search → fetch sequence. types discovers the stable canonical taxonomy and any custom headings before selecting a query. The agent must run types before every query, including a canonical-only request, then use the result without asking the user again. If types returns next_cursor, perform full type-cursor traversal with the same directory and budget before claiming that a custom type does not exist:

python "$SECTION_QUERY" types --dir "$RESEARCH_LOG_DIR" --budget 4000 --cursor "<opaque-cursor>"

Then search the selected types to receive a manifest, never full bodies:

python "$SECTION_QUERY" search \
  --dir "$RESEARCH_LOG_DIR" \
  --sections Failures Analysis "Open Problems" \
  --range 90d \
  --budget 4000

The named presets are all, 7d, 30d, 90d, and year; omitting a range uses all. Use inclusive custom dates when the request supplies a meaningful boundary, not as a substitute for history-wide repetition checks:

python "$SECTION_QUERY" search \
  --dir "$RESEARCH_LOG_DIR" \
  --sections Changes Setup Results Analysis \
  --from 2026-01-01 --to 2026-06-30 \
  --budget 4000

--range and custom --from / --to bounds are mutually exclusive. Normal use obtains the current date from the runtime; --today YYYY-MM-DD is only for reproducible automation. Follow every manifest next_cursor with the same normalized search conditions, directory, and budget before making a claim about the searched scope.

python "$SECTION_QUERY" search \
  --dir "$RESEARCH_LOG_DIR" \
  --sections Failures Analysis "Open Problems" \
  --range 90d \
  --budget 4000 \
  --cursor "<opaque-cursor>"

The default response budget is 4,000 estimated tokens. A lower positive --budget is allowed; 8,000 is the hard maximum. Every response includes its budget estimate. A budget error, malformed command, unreadable source, or stale cursor is an explicit query failure, not an empty result.

Fetch only selected manifest IDs. fetch returns every requested body atomically when the selection fits:

python "$SECTION_QUERY" fetch \
  --dir "$RESEARCH_LOG_DIR" \
  --ids "2026-05-18_bert_finetuned_full::failures" \
  --budget 4000

If the result has status: overflow, fetch each listed suggested_batches in separate calls. It deliberately returns no partial bodies. If one body is too large, the result has status: chunk_required; follow chunk_cursor using --chunk-cursor and then every next_chunk_cursor. Complete full chunk traversal before claiming the oversized section was reviewed. Preserve chunks in order to reconstruct the exact source body: never silently truncate, summarize, or omit a requested section.

python "$SECTION_QUERY" fetch \
  --dir "$RESEARCH_LOG_DIR" \
  --chunk-cursor "<opaque-chunk-cursor>" \
  --budget 4000

Historical Experience Check

Run this protocol when both conditions are true:

1. The current project contains a non-empty research log journal (see the resolved $RESEARCH_LOG_DIR from Storage above).
2. The agent is about to plan, recommend, execute, repeat, or diagnose research work.

Mode activation is not required. A direct research request such as "try this method", "change these experiment parameters", or "why did this run fail?" is sufficient intent even without an active research-mode. Do not run the check for unrelated maintenance, simple conceptual questions, prose editing, formatting, or slide layout unless a research decision or evidence reconstruction is required.

Use checks at decision boundaries, not before every action:

• at the start of a new research objective;
• before proposing a new experimental method or configuration;
• before a costly or long-running experiment;
• when an error, anomalous result, or research blockage appears; and
• before repeating an experiment or operation that may already exist in the journal.

Research intent	Initial types
New method or experiment	Goal, Setup, Results, Failures, Conclusion
Error or anomaly	Failures, Analysis, Next Steps, discovered problem-oriented custom types
Parameter or implementation change	Changes, Setup, Results, Analysis
Costly rerun	Goal, Setup, Results, Failures

Start with types so discovered problem-oriented custom types, for example Open Problems, are included when relevant. Use the query flow above and retrieve only the selected source bodies. Within one session, record normalized query conditions and the journal state fingerprint. This is session-local decision state, not a persistent index: the agent does not repeat an identical query while the relevant files remain unchanged.

After retrieval, classify the evidence without collapsing distinct cases:

• previously attempted and unsuccessful;
• previously solved with an effective fix;
• attempted with inconclusive results;
• similar but materially different in data, code version, parameters, or goal;
• no relevant record found.

"No relevant record found" applies only to the successfully searched journal and query scope; it is not evidence that no prior attempt exists elsewhere. Historical records inform a decision but do not automatically prohibit work.

• General research discussion: advisory. Disclose a query failure and continue the discussion with that limitation.
• Costly or long-running operation: preflight gate. Complete the check before execution. If the query mechanism fails, pause execution and disclose the failure; the agent must not report a query failure as empty history.
• If the journal is missing or contains no log entries, the check passes without requiring the user to create one.
• An explicit user request to reproduce or verify prior work permits execution after the history and rerun rationale are surfaced. A changed-condition experiment follows the same disclosure rule.

---

Frontmatter reference

Field	Description
date	YYYY-MM-DD
experiment	slug used in filename
tags	free-form list
summary	one-sentence description of this entry's content; always present, drafted by the agent and confirmed by the user on every add/amend
milestone	milestone ID this entry belongs to (e.g. M2); only set once milestone mode is active (see Milestone Mode)
follows	filename of prior experiment (optional)
reason_follows	why this follows from the prior (optional)
git_head	short SHA of HEAD commit when this entry was written (optional)
slide_decks	paths added by /report-slides (do not edit manually)
amended	change records added by amend command

Notes

• If $RESEARCH_LOG_DIR does not exist, create it silently — the directory itself was already confirmed by the user during role resolution; this only covers the directory not yet existing on disk. This applies only to a path that came back with status: "resolved". Never create a directory for an empty $RESEARCH_LOG_DIR, and never recreate the primary of a stale_mapping response — that path is one the user moved or deleted, and it must be re-confirmed first.
• If a follows: target file is not found, warn but continue.
• slide_decks and amended are managed by skills only — never ask the user to set them.
• git_head enables reconstructing the exact commit range for any experiment: given two consecutive entries with git_head values A and B, run git log A..B to see all changes.
• summary is drafted by the agent and confirmed by the user on every add; never left blank.
• Milestone mode (MILESTONES.md) auto-enables once total log content crosses the token threshold reported by log_stats.py (default 6000 tokens) and, once created, is always kept up to date — there is no auto-disable.
• Milestone grouping and titles are always agent-suggested and user-confirmed, never decided silently.
• A follows: chain crossing a milestone boundary is fine and not flagged — milestones are a content-grouping convenience, not a hard timeline partition.