Agent skill

Skill Shortener

by luongnv89 in luongnv89/asm

Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost.

MITAuto-check: notesAI & LLM Engineering

Install Skill Shortener

skills CLI
$ npx skills add luongnv89/asm --skill skill-shortener -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install luongnv89/asm skill-shortener --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/luongnv89/asm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-shortener .claude/skills/skill-shortener && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-shortener
GitHub stars
954
Token cost
~3.8k tokens
SKILL.md length
1,546 words
Files
9 (incl. scripts, references)
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost.

  • Works in 5 steps: Baseline → Classify every section (the manifest) → Plan and approve → …
  • Authoring new skills
  • SKILL.md covers Two modes, Run variables, Dependency Preflight (mandatory) and Snapshot and Repo Sync Before…, plus 6 more sections
  • Runs Python scripts from its folder; calls git and python3

What it does

Skill Shortener is an agent skill from luongnv89/asm. Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost. Don't use for authoring new skills, eval retrofits, or prose.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `docs/README.md`, `evals/evals.json` and `references/behavior-preservation.md`). Compatibility notes: Claude Code; Python 3; skill-creator's quickvalidate.py

It sits in AI & LLM Engineering, covering LLM cost and token optimization. The repository describes itself as: The universal skill manager for AI coding agents. The licence is MIT.

When your agent uses it

  • Authoring new skills
  • Tasks that involve LLM cost and token optimization

Example prompts

  • “/skill-shortener”

Requirements

  • Python 3
  • Node.js
  • Compatibility (from SKILL.md): Claude Code; Python 3; skill-creator's quick_validate.py
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Grep, Glob

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Baseline
  2. Classify every section (the manifest)
  3. Plan and approve
  4. Apply
  5. Verify

What it can do on your machine

Read from SKILL.md and the folder at commit a01a183. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Grep
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code; Python 3; skill-creator's quick_validate.py

    From compatibility in the SKILL.md frontmatter.

Context cost

Skill Shortener loads about 3.8k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 1,546 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Grep, Glob

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from luongnv89/asm at commit a01a183, republished under its MIT licence (© luongnv89). 1,546 words, ~3,772 tokens.

Download SKILL.mdSave it as .claude/skills/skill-shortener/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
skill-shortener
description
Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost. Don't use for authoring new skills, eval retrofits, or prose.
allowed-tools
Bash, Read, Write, Edit, Grep, Glob
compatibility
Claude Code; Python 3; skill-creator's quick_validate.py
license
MIT
effort
high
metadata.version
1.0.1
metadata.author
luongnv89

Skill Shortener

Shrink an over-long SKILL.md by progressive disclosure: what the agent needs on every activation stays in the body, everything else moves behind a pointer that says when to load it — or is cut outright.

Three load layers decide where a piece of content belongs:

LayerHoldsCosts
always-loadedfrontmatter name + descriptionevery turn
on-triggerthe SKILL.md bodyevery activation
on-demandreferences/, scripts/, assets/only when read

The bar is behavior-preserving: the shortened skill drives the same process as the long one. Line count is the score, not the goal — a run that reaches 400 lines by deleting a loop's stop condition has failed, however good the number looks.

Two modes

Pick one before Phase 0; they diverge at Phase 2.

  • Mode 1 — shorten (default). Phases 0–4: measure, classify, plan, apply on approval, verify. Every "shorten this", "this SKILL.md is too long", "cut its token cost", "apply progressive disclosure" request is Mode 1.
  • Mode 2 — audit only. Phases 0–2, then stop and hand over the plan. Nothing outside the workdir is written, so the repo sync and snapshot in Phase 0 are skipped. Use when the user wants to know what would move, or will apply the split themselves.

Run variables

Every snippet below uses these four. Bash tool calls do not share shell state, so re-declare them at the top of each call that needs them — an empty $SKILL_PATH turns cp -R "$SKILL_PATH/." into a copy of the filesystem root. Capture RUN_EPOCH once and reuse the number; never re-stamp it.

bash
SS="$HOME/.claude/skills/skill-shortener"                            # this skill
SKILL_PATH="$HOME/.claude/skills/<target>"                           # the target: the dir holding SKILL.md
QV="$HOME/.claude/skills/skill-creator/scripts/quick_validate.py"    # the Phase 4 gate
# RUN_EPOCH: reuse the number the preflight printed, e.g. RUN_EPOCH=1787948450

Dependency Preflight (mandatory)

This skill invokes skill-creator: it runs that skill's quick_validate.py as the Phase 4 frontmatter gate. Resolve it before the snapshot below, the first step that changes anything:

bash
RUN_EPOCH="$(date +%s)"; echo "run_started_epoch=$RUN_EPOCH" >&2   # anchors Run stats
QV="$HOME/.claude/skills/skill-creator/scripts/quick_validate.py"
test -f "$QV" || {
  echo "Missing required skill: skill-creator" >&2
  echo "Install it:      asm install skill-creator -p claude --yes" >&2
  echo "No asm yet:      npm install -g agent-skill-manager" >&2
  echo "Verify:          asm list -p claude --json | grep 'skill-creator'" >&2
  exit 1
}

-p claude is not decoration: asm install refuses to guess a provider non-interactively and --yes does not cover that choice, so an install command without it errors instead of installing. On a miss, stop before the first mutation and print those three commands — never continue with a partial run.

Snapshot and Repo Sync Before Edits (mandatory)

Always snapshot first. The most common target — ~/.claude/skills/<name>/ — is not a git repository, so git is not a guaranteed undo path, and this skill rewrites a whole directory rather than one file:

bash
: "${SKILL_PATH:?set SKILL_PATH to the target skill directory}"
: "${RUN_EPOCH:?reuse the epoch captured in the preflight}"
case "$PWD/" in "$SKILL_PATH"/*) echo "cwd is inside $SKILL_PATH — run from outside it, or the workdir is copied into itself" >&2; exit 1;; esac
SNAP=".skill-shortener/snapshot-$RUN_EPOCH"
mkdir -p "$SNAP" && cp -R "$SKILL_PATH/." "$SNAP/"

Then sync only if the target directory itself is a git work tree root — rev-parse --git-dir succeeds for any nested path inside a larger repo, and must not trigger fetch/pull of that tree:

bash
toplevel="$(git -C "$SKILL_PATH" rev-parse --show-toplevel 2>/dev/null || true)"
if [ -n "$toplevel" ] && [ "$(cd "$SKILL_PATH" && pwd -P)" = "$toplevel" ]; then
  branch="$(git -C "$SKILL_PATH" rev-parse --abbrev-ref HEAD)"
  git -C "$SKILL_PATH" fetch origin && git -C "$SKILL_PATH" pull --rebase origin "$branch"
else
  echo "note: $SKILL_PATH is not a git work tree root — $SNAP is the only undo path; skip fetch/pull"
fi

If the tree is dirty, git stash, sync, git stash pop. If origin is missing or the pull conflicts, stop and ask the user — never skip or force the sync. In a git repo, suggest adding .skill-shortener/ to .gitignore.

The three dispositions

Every section of the body gets exactly one, and each has one reference behind it. Read the reference for the disposition you are about to assign — not all three up front.

DispositionMeansRead before assigning it
KEEPneeded on every activation; stays in the bodyread references/behavior-preservation.md before assigning it
CUTdeleted; the model already knows it or it changes no behaviorread references/cut-list.md before assigning it
MOVErelocated to references/, scripts/, or assets/, with a pointerread references/split-patterns.md before assigning it

Workflow

Phase 0 — Baseline

Mode 2 — skip snapshot and git sync. Audit-only must not reach fetch / pull --rebase. Run the preflight and the two measure_skill.py commands below, then continue to Phase 1. Do not create $SNAP and do not run the repo-sync block.

Mode 1. Run the preflight, take the snapshot, sync only when $SKILL_PATH is itself the git work tree root (see above), then measure:

bash
python3 "$SS/scripts/measure_skill.py" "$SKILL_PATH" --json --out .skill-shortener/baseline.json
python3 "$SS/scripts/measure_skill.py" "$SKILL_PATH"   # the human-readable section table

The section table, largest first, is where the fat is — read it before reading the body.

Early exit. If the verdict is WITHIN_CAP, stop: print the current footprint and say no shortening is warranted — splitting a body that already fits costs a pointer hop for no saving. The one exception is an existing references/ tree that is chained or orphaned; see Edge cases. The user can override.

Done when: .skill-shortener/baseline.json exists with a non-empty sections array, and the verdict is recorded.

Phase 1 — Classify every section (the manifest)

Read the body, then assign every heading from baseline.json exactly one disposition. Write .skill-shortener/manifest.json:

json
{
  "target": "<skill-path>",
  "sections": [
    { "heading": "Overview", "disposition": "KEEP" },
    {
      "heading": "API error codes",
      "disposition": "MOVE",
      "destination": "references/<topic>.md",
      "load_condition": "when the API returns a non-200"
    },
    {
      "heading": "History",
      "disposition": "CUT",
      "reason": "changelog and attribution; changes no behavior"
    }
  ]
}

destination may be a string or a list. load_condition is the clause that goes into the pointer, so write it as the agent will read it: "when X", "before Y", "if Z".

The manifest is the loss-prevention record. Nothing is verified as preserved except through it, so an unclassified section is an unaudited deletion waiting to happen.

Done when: every heading in baseline.json appears in the manifest exactly once; every CUT carries a reason; every MOVE carries a destination and a load_condition. scripts/verify_shorten.py checks all four in Phase 4 — do not defer them.

Phase 2 — Plan and approve

Present the plan as a table — heading, disposition, destination, lines saved — plus the projected body size against both caps and the always-loaded/on-demand split. Name the biggest three savings first.

Expected output — the plan, before any file is touched:

Plan for skill-x — 812 lines / 5,140 words → projected 190 lines / 1,480 words

  heading                  disposition  destination                   lines
  -----------------------  -----------  ----------------------------  -----
  Azure deployment         MOVE         references/<branch>.md         -180
  Error code reference     MOVE         references/<codes>.txt         -140
  Release history          CUT          changelog; changes no behavior  -46
  Workflow                 KEEP         -                                 0

  always-loaded 64 tokens (unchanged) · on-trigger 12,900 → 3,700 · on-demand +9,100

Mode 2 ends here. In Mode 1, stop and wait for an explicit go-ahead. A split is a judgment call and the user is the one who has to live with the result.

Done when: the user has approved the plan, or asked for changes that are folded back into the manifest.

Show full SKILL.md (662 more words)Show less
Phase 3 — Apply
  1. Write each MOVE destination. The moved material must be self-contained — a reader arriving with only the pointer's context can act on it. Add a one-line contents map at the top of any reference over 300 lines.
  2. Rewrite the body: delete CUT sections, replace each MOVE section with a pointer carrying its load_condition ("Read references/<topic>.md when the API returns a non-200"). A bare "see references/<topic>.md" is relocation, not disclosure, and Phase 4 fails it.
  3. Keep references one level deep. A reference that points to another reference is a chain — inline it or promote it to a sibling.
  4. Bump metadata.version: minor for pure relocation, major if any CUT removed an instruction or the step sequence changed.

When the plan extracts three or more reference files and the Agent tool is available, hand each one to its own worker: the worker's Input is references/split-patterns.md plus that section's text, and it returns the finished file. The body rewrite stays with the main agent, which is the only step that needs the whole picture.

Done when: every MOVE destination exists and is non-empty, the body contains a conditional pointer to each, and the version is bumped.

Phase 4 — Verify
bash
python3 "$SS/scripts/verify_shorten.py" "$SKILL_PATH" \
  --manifest .skill-shortener/manifest.json \
  --baseline .skill-shortener/baseline.json
python3 "$QV" "$SKILL_PATH"

Then do the read-back, which no script can do for you: open every file the plan created and confirm the material arrived complete and reads as instructions rather than as an excerpt. The script proves the manifest is exhaustive and the wiring is sound; only the read-back proves the content survived the move.

On a failure, fix and re-run — up to 3 rounds, then report what still fails instead of looping. If the result is worse than the original, restore — guarding both variables, because an empty $SNAP leaves a deleted skill and nothing to put back:

bash
: "${SKILL_PATH:?}"; : "${SNAP:?}"; test -d "$SNAP" || { echo "no snapshot at $SNAP" >&2; exit 1; }
rm -rf "$SKILL_PATH" && cp -R "$SNAP" "$SKILL_PATH"

Done when: verify_shorten.py exits 0, quick_validate.py exits 0, and every created file has been read back.

Edge cases

Only the ones where judgment reliably goes wrong — everything else, handle on the merits.

  • Already within both caps → take the Phase 0 early exit. Splitting a body that fits adds a pointer hop and saves nothing.
  • The target already has references/ → measure it too. A chained or orphaned existing tree is in scope even when the body fits, and it is the one case where a WITHIN_CAP verdict still warrants work.
  • The path is a collection (no SKILL.md at the root, children have one) → ask which skill. Never span two skills in one manifest.
  • The user asks to cut a never-cut block → say in one sentence why it stays, then pursue the size target through the other dispositions. Do not silently comply, and do not refuse the whole task.
  • A section too big for one destination → destination takes a list. Split it by sub-topic, never by line range: half a procedure is not self-contained.
  • Under one cap, over the other → both gate. 480 lines at 4,200 words has not been shortened enough.

Step Completion Reports

After each phase, print:

◆ [Phase Name] (phase N of 4 — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          × fail — [reason]
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Per-phase checks: Phase 0 Preflight, Snapshot, Baseline measured, Cap verdict. Phase 1 Every section classified, Reasons given, Load conditions written. Phase 2 Plan presented, Approval received. Phase 3 Destinations written, Pointers conditional, Version bumped. Phase 4 verify_shorten, quick_validate, Read-back.

Run stats (mandatory)

Close every run — including an early exit, a refused gate, or a failed phase — with this block as the last thing printed:

  ┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
  Run stats   elapsed 4m 12s · tokens 96,300 · cost $0.31
              agents 0 · skills 1 · tool calls 22

Fields are fixed and in this order: elapsed, tokens, cost, agents, skills, tool calls. tokens and cost are omitted entirely when the host reported no figure — never estimated. The other four always print; an undeterminable value prints n/a, and 0 is a determined value. elapsed comes from RUN_EPOCH, captured once in the preflight.

Reference files

FileRead it when
references/behavior-preservation.mdyou are assigning KEEP — it names the blocks that may never be cut, whatever they cost
references/cut-list.mdyou are assigning CUT — what is safe to delete and how to word the reason
references/split-patterns.mdyou are assigning MOVE — choosing the destination, writing the pointer, flattening

© luongnv89, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in skills/skill-shortener of luongnv89/asm.

  • SKILL.md
  • docs/README.md
  • evals/evals.json
  • references/behavior-preservation.md
  • references/cut-list.md
  • references/split-patterns.md
  • scripts/measure_skill.py
  • scripts/test_condition_cues.py
  • scripts/verify_shorten.py

Open the folder on GitHubat commit a01a183

Compare with similar skills

Skill Shortener next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skill Shortener compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Shortener this skillluongnv89/asm954—~3.8kAutomated safety check: NotesMIT
Context Compressionguanyang/open-agent-hub9772 repos~4.6kAutomated safety check: PassMIT
Bounty Hunter1sadjlk/bounty-hunter-skill2821 repos~761Automated safety check: PassMIT
Fleet Auditoralexgreensh/token-optimizer2.5k—~1.7kAutomated safety check: PassCustom licence
Context Auditundefined-ui/second-brain-os1k—~802Automated safety check: PassMIT
Headroommomori777/Artemis380—~562Automated safety check: PassMIT

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Questions about Skill Shortener

What does Skill Shortener do?

Refactor a too-long SKILL.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost. Skill Shortener is an agent skill from luongnv89/asm.md by progressive disclosure: measure token cost, classify every section KEEP/CUT/MOVE, shorten the body into references/ and scripts/, verify nothing was lost.

When should I use Skill Shortener?

Skill Shortener fits situations like: authoring new skills; tasks that involve LLM cost and token optimization.

How do I install Skill Shortener in Claude Code?

Run `npx skills add luongnv89/asm --skill skill-shortener -a claude-code`. Or copy the skill folder (skills/skill-shortener in luongnv89/asm) into .claude/skills/skill-shortener in your project. Claude Code loads it when a task matches its description.

How do I install Skill Shortener in Codex?

Run `npx skills add luongnv89/asm --skill skill-shortener -a codex`. Or copy the skill folder (skills/skill-shortener in luongnv89/asm) into .agents/skills/skill-shortener in your project. Codex loads it when a task matches its description.

Can I use Skill Shortener in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add luongnv89/asm --skill skill-shortener -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-shortener, .gemini/skills/skill-shortener, .github/skills/skill-shortener and .opencode/skills/skill-shortener in your project.

What does Skill Shortener need to run?

Going by SKILL.md and its folder, Skill Shortener needs Python for the scripts in its folder and the command-line tools its instructions call (git and python3). Our summary lists: Python 3; Node.js. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Grep, Glob. Compatibility (from SKILL.md): Claude Code; Python 3; skill-creator's quick_validate.py.

Does Skill Shortener access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Skill Shortener safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Skill Shortener use?

Skill Shortener is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Shortener use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Skill Shortener?

Skills that share tags, products or a category with Skill Shortener: Context Compression (guanyang/open-agent-hub, 977 stars), Bounty Hunter (1sadjlk/bounty-hunter-skill, 282 stars), Fleet Auditor (alexgreensh/token-optimizer, 2.5k stars) and Context Audit (undefined-ui/second-brain-os, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Shortener?

luongnv89 (a GitHub user) maintains it in luongnv89/asm, which has 954 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 6, 2026.

Source: luongnv89/asm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.