accenture/mercury/refresh-metadata

Claude Code and agent skill by accenture/mercury

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Recompute continuity.md fact footers (last_used, uses, tier) from the session reference log — REVIEW.md steps 2–3, done deterministically. Use during a review, or whenever memory-lint reports [stale-metadata], instead of updating footers by hand. Pure arithmetic from the references; it never decides what to archive.

Install

npx vskill@latest install accenture/mercury/refresh-metadata

SKILL.md

⚠️ Tool-managed skill provided by agent-memory (provenance: agent-memory-builtin). Don't edit it
in place — fork under a new name, or upstream a fix to the agent-memory project (see SKILLS.md).

This skill performs REVIEW.md steps 2–3 (apply events + re-tier) as a runnable script. For every fact in continuity.md and every `memory/open-threads/thread-<id>.md` (v4.39.0) it recomputes last_used, uses, and tier from the ## Memory References across memory/sessions/, and writes the footers back. This is the "full rebuild" path REVIEW.md already calls "deterministic and reproducible by any agent" — pure arithmetic, no judgment — so it's safe to mechanize. Agents routinely skip this pass (they archive faded facts but don't re-tier the ones that stay), leaving stale footers; a cross-vendor review proved it. This closes that gap.

What it does NOT do

  • It does not decide what to archive — that's archive-fact plus the agent's judgment

(never-pick-a-winner). It clamps tier at archive-candidate; a fact still in continuity is never marked archived (that tier means moved).

  • It does not touch core or superseded facts (human-set / terminal), or facts with no reference

in any session log (legacy — can't recompute, so preserved), and it never adds missing fields.

  • For a pinned `- [ ]` open thread it refreshes the factual fields (uses / last_used) but **leaves

the tier label as-is** (v4.26.1) — pinned-ness protects an open thread, not its tier, so the tool doesn't opine on it (a working-tagged open thread is fine).

So the division of labor across the three memory tools mirrors the meaning/mechanics split: memory-lint verifies (read-only) · refresh-metadata re-tiers (arithmetic) · archive-fact moves (the decided archival). Only which facts to archive needs the agent.

How to run

From the repo root, whichever runtime the machine has (output is byte-identical):

sh
python3 agent-skills/refresh-metadata/scripts/refresh-metadata.py [--dry-run]
# or
node agent-skills/refresh-metadata/scripts/refresh-metadata.mjs [--dry-run]
  • --dry-run — print the footers that would change (tier / uses), change nothing. Preview first.

It reads each fact surface (continuity.md + the thread files) into memory and writes each once (truncate-before-read is impossible); thread files refresh in place, so their merge-free filenames never churn. For each fact: uses = number of sessions that reference it; last_used = the latest such session's date; tier = DECAY.md §5 applied to sessions_since_last_used (clamped at archive-candidate). Idempotent — a second run reports "nothing to refresh."

Run `memory-lint` afterward — the [stale-metadata] advisories should clear. Facts that come out archive-candidate and are past archive_window will still show [overdue]; archive those with archive-fact (the move it can't make for you).

Notes

  • No runtime? Don't hand-edit 30 footers (error-prone, and the truncation trap lurks). Install Python or

Node, or do the review's metadata pass carefully by hand per REVIEW.md (read-into-var, never truncate-first).

  • Two runtimes at output parity, with mirror tests (test_refresh_metadata.py / .mjs).

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
91
Repository forks
40
7-day trend score
23

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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