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kev-hukev-hu/ai-toolkit/skills/recall/SKILL.md

recall

Skill0 starsChanged 4 months agoMITView on GitHub

Unified retrieval across your durable-knowledge surfaces (learnings ledger, session notes, lessons/taste files, agent memory). Use before substantial work, when a topic smells previously-solved, or when the user asks "have we seen this before?" / "what do we know about X?".

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The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 129 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Query with 2–4 concrete keywords, not sentences: If the user's phrasing is vague, reshape it before querying (e.g.

    line 31

“Common” is measured: kept by at least 6 in 10 of the 129 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

recall — one front door for durable-knowledge retrieval

Searches every knowledge surface declared in a config file with one query and ranks results by match quality (distinct query terms hit), not confidence or recency. One command, grouped plain-text output, built for agents to read.

Setup (once per machine)

  1. Copy config.example.json (in this skill folder) to ~/.config/recall/config.json.
  2. Edit the path of each surface to point at your real files; delete surfaces you don't have. Every surface is optional — unreadable paths are skipped silently.
  3. Optional: set log to a JSONL path to record queries (off by default).

Usage

<this-skill-dir>/scripts/recall <query terms...> [--config <path>]

Config resolution: --config → $RECALL_CONFIG → ./recall.config.json → ~/.config/recall/config.json.

For a human-friendly PATH install: ln -s <this-skill-dir>/scripts/recall ~/.local/bin/recall.

How to use it well (agent protocol)

  1. Query with 2–4 concrete keywords, not sentences. If the user's phrasing is vague, reshape it before querying (e.g. "that thing where uploads broke" → upload timeout s3).
  2. Read the top 1–2 hits deeper if needed. Session/note hits are file paths — open them. Ledger hits may have longer notes elsewhere in your setup.
  3. Synthesize; don't dump. Answer what the user is actually trying to recall. Cite ledger entries that materially shape the answer (e.g. Prior learning applied: <key> — or whatever the config's footer says).
  4. On a miss, say so plainly and suggest 1–2 alternate query terms; a surface-empty result is information too.

Reading the output

## ledger (3)
- [gws-exit-codes] gws CLI exit 2 = auth, exit 3 = path validation  [match 3/3]

[match n/m] = n of your m query terms hit this row. A 3/3 on one surface beats a high-confidence 1/3 anywhere — that is the point. Per-surface caps (config cap, default 4) keep any one surface from flooding the output.

Surface types

typeone row isgood for
jsonla JSON line (matchFields, label template, tiebreakField)structured learning ledgers
md-bulletsa - bullet in one fileLESSONS.md / TASTE.md-style curated lists
md-dira .md file in a directory (filePattern, exclude, matchFilename, labelFrom: "description")session notes, agent memory dirs

Design invariant

Ranking is match-quality-first, tiebreak second. Do not "improve" this to confidence-first or recency-first ranking — that ordering was a diagnosed precision failure in this tool's ancestor. See the comment in scripts/recall.

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