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Hyper memory

Skill zeikar/hyperclaude/skills/hyper-memory

Use on-demand to extract evidence-anchored repo-local knowledge candidates from accumulated .hyperclaude/ artifacts (plans/done, plan-reviews, research) and curate them. Also when the user invokes /hyperclaude:hyper-memory. Orchestration-only — no Codex spawn.From its SKILL.md

Install
npx -y skills add zeikar/hyperclaude --skill hyper-memory

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 3 commands, including `node "${CLAUDE_PLUGIN_ROOT}/scripts/memory/extract.mjs"` and 2 more.

SKILL.md

5.3 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

hyper-memory

Repo-local knowledge extraction. Scans the accumulated .hyperclaude/ corpus and writes one evidence-anchored candidate markdown file per deterministic copy-based span under .hyperclaude/memory/candidates/. v1 is extraction + curation only — auto-injection into future sessions is the v2 north star and is explicitly out of scope here.

When to use

  • User typed /hyperclaude:hyper-memory (with or without an argument).
  • A batch of work has accumulated in .hyperclaude/ (several archived plans, plan-reviews, research artifacts) and it's worth mining for durable repo-local knowledge.

When to skip

  • Only a single small artifact exists since the last extraction — not enough accumulated corpus to be worth mining.
  • You want the knowledge injected automatically into a session — that's v2, not implemented.

How it works

  1. Run node "${CLAUDE_PLUGIN_ROOT}/scripts/memory/extract.mjs" via Bash and parse the one-line JSON summary it prints to stdout: { ok, scanned, candidates, written, skipped, errored, candidatesDir }.

    The script's CLI accepts exactly two flags — no others exist:

    • --dry-run — compute candidates and keys but write nothing (written is always 0).
    • --root <path> — corpus root to scan (default .hyperclaude).
  2. It fully enumerates the v1 source allowlist — NOT newest-only:

    • plans/done/ — every archived plan.
    • plan-reviews/ — every plan-review artifact whose verdict is Ship as-is.
    • research/ — every research artifact.

    code-reviews/ and docs-reviews/ are v1 non-goals and are never scanned.

  3. It writes one evidence-anchored markdown file per candidate under .hyperclaude/memory/candidates/, keyed by a compound hash so re-runs are idempotent: a candidate is skipped if its key already exists in EITHER .hyperclaude/memory/candidates/ OR .hyperclaude/memory/promoted/ — an already-promoted candidate is never resurrected.

Candidate schema

Each candidate file's YAML frontmatter carries exactly these keys, in this order:

plugin-version, type, source-artifact, anchors, mode, slug, git-head, generated, staleness

followed by a ## Claim (a deterministically templated one-liner) and a ## Evidence section holding strictly the verbatim copied span — never a generated or derived line.

CORE POLICY: artifact sentences are never stored as truth on their own — every candidate carries an inline evidence anchor quoted verbatim from source-artifact:, so a claim can always be traced back to the exact text it came from.

Two fields are easy to conflate and must be read as distinct:

  • source-artifact: — the .hyperclaude/** artifact path the candidate was mined from (evidence provenance; a gitignored artifact, not a canonical repo source).
  • anchors: — a YAML list of live canonical repo source/doc paths the claim is about. The extractor ALWAYS emits anchors: [] — none of the three v1 sources deterministically names a real repo file, and a .hyperclaude/** path is NEVER a valid anchors: entry.

Curation

Two locations only — no multi-state machine:

  • .hyperclaude/memory/candidates/ — proposed, unreviewed.
  • .hyperclaude/memory/promoted/ — human-accepted.

Promote: plain mv .hyperclaude/memory/candidates/<file> .hyperclaude/memory/promoted/<file> — NOT git mv (.hyperclaude/ is gitignored, so git tracks neither side).

Promotion gate: every candidate ships with anchors: []. Before promoting, the curator MUST add at least one real repo source/doc path (a non-.hyperclaude/ file that exists on disk) to the candidate's anchors: list. source-artifact: provenance alone never satisfies this gate — it names a gitignored artifact, not a canonical anchor.

Reject: rm the candidate file.

Idempotency: because promotion is a plain move out of candidates/, and the extractor checks BOTH candidates/ and promoted/ for an existing key, a promoted candidate is never re-created by a later extraction run.

Invocation argument

Invocation argument: $ARGUMENTS

Accepted argument grammar — nothing outside this table:

Token(s)Meaning
--dry-runcompute candidates/keys, write nothing
--root <path>corpus root to scan (default .hyperclaude)

The script's CLI parser rejects anything else (unknown flags, --root with a missing/flag-like value) with {"ok":false,"error":...} and a non-zero exit — see scripts/memory/extract.mjs.

Do NOT interpolate the raw $ARGUMENTS string into the Bash command. Parse it into individual tokens, keep only tokens matching the grammar above, and pass each as its own shell-quoted argument (e.g. quote the --root path value) when invoking node "${CLAUDE_PLUGIN_ROOT}/scripts/memory/extract.mjs". Any token outside the grammar means: do not pass it through — the script would reject it anyway.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most memory context skills give in ~1.2k tokens

Counted across 754 of the 1,056 authors here whose files we hold, read 2026-09-06

  • Preserve existing content structurein 15 of 754, across 9 files
  • Front-load the leading wordin 14 of 754, across 10 files
  • Update existing entries instead of duplicatingin 14 of 754, across 7 files
  • Keep CLAUDE.md under one hundred linesin 14 of 754, across 12 files
  • Read CLAUDE.md at the project rootin 14 of 754
  • Keep each meaning in a single source of truthin 12 of 754, across 8 files
  • Redact sensitive information before committingin 11 of 754, across 4 files
  • Scan for all CLAUDE.md filesin 11 of 754, across 7 files
  • Use frontmatter for metadata on filesin 10 of 754, across 3 files
  • Repeat user interactions 10 timesin 10 of 754, across 4 files
  • Write the CLAUDE.md file into the target folderin 10 of 754, across 8 files
  • Use memlab to process snapshotsin 9 of 754, across 3 files

Said here and by no other author read

  • Run the extraction script via Bash
  • Parse the one-line JSON summary
  • Write one candidate markdown file per span
  • Add at least one real repo path before promoting
  • Move candidates to promote them
  • Remove candidate files to reject them

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.