本文へ移動
cccskills
無料GitHub で公開

memoff-memory-report

Use at the Mem Off hackathon. Keeps a running, evidence-backed report on how the participant's memory system performed for their coding agent during the day — what the harness measured, what cognee-report's scorecard says, what was recalled and whether it helped — in ./memoff-report.md and ./memoff-report.json. The report is submitted together with the action results; it never contains quotes, keys, data or names.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md6.8 KB
  • hook.sh3.0 KB
  • install.sh4.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Keep the Mem Off memory report current

You are the participant's coding agent, so you see every run they make and every recall their memory system produces. Mem Off ranks memory systems on a frozen harness, but the organisers also want the participant's own evidence: what the memory did for them during the day, in numbers from the tools, not in impressions. Keep that in two files and finish them at wrap-up. Unlike cognee-feedback.md, these two files are submitted: they go into submissions/<team-slug>/report/ next to the action results.

Rules that never bend:

  • Never slow or block their build. If any step here fails, skip it and say so in the file.
  • Numbers come from tools (memoff, plugin_eval.py, cognee-evidence.py, the entrant's receipt.sh), never from memory of the conversation. Quote the command and the figure.
  • No quotes of the participant, no keys, no .env contents, no document or code text, no names or handles. Ids, ranks, counts, durations and booleans only.
  • Smoke results (no_model_smoke) are orchestration proof, never a memory effect: label them.
  • A recalled or injected item is not proof it changed an answer. Record "helped" only when the participant or a diff shows it (the agent used a file, symbol or decision it recalled).

Cadence: checkpoints, not constant bookkeeping

About every 15 minutes a hook ends your turn with a "memoff-report checkpoint" message carrying what it found since the last checkpoint: new memoff run folders, whether the cognee plugin and its cognee-report skill are present, and the current-session recall counters. That message is your cue. At a checkpoint, spend one short step on the files:

  1. for each new run folder: read its action-run.json, metrics.json and leaderboard.md; add or update one row per run in Runs (arms, issues, repeats, all-pass, median cost, turns, injected chars, smoke yes/no);
  2. if the cognee plugin is present, refresh Scorecard with python3 "$PLUGIN/skills/cognee-report/scripts/plugin_eval.py" report --since today --format json --out ./.memoff-scorecard.json (keep hit rate, recall and SessionStart latency, saves, injected chars, grade, sample counts; mark low-sample metrics unmeasured, exactly as the tool does);
  3. add one Recall observations line for anything recalled in this stretch that the participant acted on or clearly ignored: issue number or task, what kind of item (file, symbol, decision, lesson), its rank from the receipt, helped yes/no/unknown, one neutral reason;
  4. if nothing memory-related happened, change nothing.

Then stop. A checkpoint touches at most 10 lines; the whole report stays under ~150.

Where the facts come from

Harness runs. Every memoff run or memoff pilot writes a folder with action-run.json. Default locations the hook scans: ./memoff-runs/ and $MEMOFF_RUNS_DIR. From metrics.json: arms.<arm>.all_pass, arms.<arm>.cumulative.total_cost_usd.median, .turns.median, .injected_chars.median, relative_to_control. From action-run.json: model, claude_code, smoke, xitkit.revision.

cognee-report scorecard (cognee plugin only; other systems: write scorecard: n/a). Find the plugin with claude plugin list or ls ~/.claude/plugins/*/*/skills/cognee-report. Then:

PLUGIN=<that plugin root>
python3 "$PLUGIN/skills/cognee-report/scripts/plugin_eval.py" report --since today --format json --out ./.memoff-scorecard.json
python3 "$PLUGIN/scripts/_session_stats.py" --days 1              # exact injected/deduped counters
python3 "$PLUGIN/scripts/cognee-evidence.py" explain --session-key <host-session-id>   # why items were / were not injected

Other memory systems. Their receipt.sh (from memory.yml) prints a JSON receipt for the last session; record ids, ranks and reasons from it. No receipt → receipt: unavailable.

The participant's diffs. git diff --stat after a turn shows whether a recalled file or symbol was actually touched. That is the only honest source for "helped".

The files

./memoff-report.md (human) and ./memoff-report.json (machine), created on the first checkpoint, updated in place. The JSON mirrors the sections below with the same keys.

---
type: memoff-memory-report
event: <event name from the "memoff report" section of AGENTS.md / CLAUDE.md>
team: <team slug from the same section>
system: <memory system name and version from memory.yml, or "none / control">
agent: <claude-code | codex | other>   cognee_plugin: <version or absent>
first_green_run: <time or never>   runs: <n>   smoke_runs: <n>
---

# Summary
<3–5 lines, written at wrap-up: what was run, what the memory measurably did, the clearest
"helped" and the clearest "did not help", and the one number you would show a judge.>

# Runs
| time | command | out dir | arms | issues | repeats | all pass | median cost | turns | injected chars | smoke |
|---|---|---|---|---|---|---|---|---|---|---|

# Scorecard (cognee-report)
<the tool's figures with sample counts, or `n/a` for non-cognee systems; grade withheld
stays withheld — never turn a withheld grade into a positive one>

# Recall observations
| time | issue/task | item kind | rank | helped | reason |
|---|---|---|---|---|---|

# Costs
<agent cost vs memory-system cost per run, from metrics.json; null = unmeasured, never 0>

# What did not work
<one line each: a recall that pointed at a stale location, a wait.sh timeout, an empty
injected dump, a hook block over 10,000 chars (Claude Code stubs it) — with the run folder>

Wrap-up

When they say they are submitting, done, or out of time — or ask for it — finish the files:

  1. Re-scan the run folders and refresh Runs; refresh Scorecard once more.

  2. Fill the front-matter counters and write the Summary.

  3. Copy both files into the submission folder: cp memoff-report.md memoff-report.json submissions/<team-slug>/report/ (create the folder). They are part of the PR; the action results are attached by the organisers next to them.

  4. Tell them, once:

    Your memory report for Mem Off is in memoff-report.md / memoff-report.json and copied into submissions/<team-slug>/report/. It has only tool output: run metrics, the cognee-report scorecard, recall receipts and whether recalled items were used — no quotes, keys, data or names. Check it, then include it in your submission PR.

Never send it anywhere else yourself.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use at a cognee hackathon. Watches the participant's experience with cognee throughout the session — errors, slowness, confusion, workarounds, and signs of frustration in how they work with you — and keeps one private feedback file they send to the cognee team at the end.

日本語の概要は準備中です。原文の説明を表示しています。

topoteretes/cognee-hackathons112026年10月18日 更新

PR Rescue

無料

Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.

日本語の概要は準備中です。原文の説明を表示しています。

topoteretes/cognee-hackathons112026年10月18日 更新

Build spend analytics and dashboards from Qdrant vector data. Covers bulk scroll extraction, aggregation patterns, vendor grouping, and Chart.js visualization. Use when building analytics features, aggregating procurement data, or creating dashboards.

日本語の概要は準備中です。原文の説明を表示しています。

topoteretes/cognee-hackathons112026年10月18日 更新

Detect anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.

日本語の概要は準備中です。原文の説明を表示しています。

topoteretes/cognee-hackathons112026年10月18日 更新

Semantic search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.

日本語の概要は準備中です。原文の説明を表示しています。

topoteretes/cognee-hackathons112026年10月18日 更新

topoteretes のスキルをすべて見る

このスキルの問題を報告する