Terse-MD
Compress Claude instruction files. Verified lossless by meaning, not by wording.
What it does
Terse-MD is a Claude Code plugin that compresses human-written Markdown instruction files (CLAUDE.md, memory files, SKILL.md) into dense YAML conforming to a closed schema. The YAML is what Claude loads on subsequent sessions. Compression is verified by round-tripping the YAML back to prose and asking you to approve the reconstruction. Wording will shift. Meaning should not — but see The honest caveats below, because meaning can drift, and the whole design assumes you'll catch it.
Compression ratios vary by content shape. Rule-heavy files (CLAUDE.md, SKILL.md, feedback memories shaped as "rule + why + how-to-apply") can see 50–70% reduction. Reference files with paths/facts/rationale typically see 15–25% at best, and they lose meaningful context in the process. Short files (<300 tokens) may not shrink at all — the YAML scaffolding costs more than the prose you're compressing.
Requirements
Terse-MD runs inside Claude Code. The validation step in /terse-md:run and the canonicalization step in /terse-md:test shell out to Python. You need:
python3PyYAML(pip install pyyaml)jsonschema(pip install jsonschema)
If pyyaml or jsonschema is missing, /terse-md:analyze falls back to a structural closure check of the compressed YAML's top-level keys against the schema. /terse-md:run and /terse-md:test both require PyYAML — /terse-md:run uses it to inject meta.source_hash into the validated YAML, and
/terse-md:test uses it to canonicalize both runs before diffing. Both commands error out cleanly at the first Python call if PyYAML is missing.
Install
From inside Claude Code:
/plugin marketplace add marcelopaniza/terse-md
/plugin install terse-md@marcelopaniza-terse-md
/reload-pluginsOr for local development, clone the repo and start Claude Code with the plugin directory attached:
git clone https://github.com/marcelopaniza/terse-md
claude --plugin-dir ./terse-mdUse
/terse-md:analyze .Scans . for candidate files, counts tokens (bytes ÷ 3.5 estimate), runs one real compression on the largest file, and prints projected savings. Writes nothing.
Scanned 4 files, 3,700 tokens total.
Sample compression on examples/larder_claude.source.md: 1,700 → 600 tokens (65% saved).
Estimated total after compression: ~1,300 tokens.
Top candidates by size:
1. examples/larder_claude.source.md 1,700 tokens
2. docs/SKILL.md 900 tokens
3. CLAUDE.md 800 tokens
4. memory/feedback_bar.md 300 tokens
Skipped by default (re-run with --include-all to include):
MEMORY.md index file; already one-liners
memory/project_foo.md narrative scratchpad; short half-life
Next: `/terse-md:run --all .` or `/terse-md:run <file>`/terse-md:run examples/larder_claude.source.md --dry-runRuns the full pipeline on one file: normalize → compress → decompress, then shows the normalized source alongside the prose reconstructed from the YAML. You approve, reject, or skip. --dry-run performs every step but never writes the .approved.yaml file.
/terse-md:test examples/larder_claude.source.mdIdempotence check. Runs the pipeline twice with fresh subagents, canonicalizes both YAMLs (sorted keys, JSON form), and diffs them. Passes if identical. Useful after changing prompts or schema.
Which files to compress
Terse-MD's closed schema works best on files that are stable (you don't edit them weekly) and structured (rules, facts, pointers — not flowing narrative). By default, Terse-MD picks up files likely to fit that profile and skips files that don't.
Good fits (picked up by default)
CLAUDE.md— global or project instructions. High re-read frequency
(loaded every session), rule-shaped content, long-lived. Compression costs amortize over hundreds of reads. This is the target Terse-MD was designed for.
SKILL.md— skill definitions. Similar shape and lifetime.feedback_*.md— Claude auto-memory "feedback" type. Already shaped
as "rule + why + how-to-apply" which maps cleanly to the schema.
The user_.md, feedback_.md, and reference_*.md patterns also match similarly-named files outside Claude's auto-memory directory (e.g. a
user_guide.md or reference_api.md in a docs folder). If that's not what you want, narrow the scan path — the patterns are filename prefixes, not directory-scoped.
Weak fits (picked up but drift is likely)
reference_*.md— reference material with paths, URLs, facts, and
rationale. The closed schema has rules/transforms/triggers/thresholds but no native "fact" or "context" bucket, so standalone paths and "why this exclusion matters" explanations tend to get dropped or folded into rule directives. Expect 15–25% size reduction and meaningful context loss. Read the review carefully. Probably not worth compressing unless the file is very large and very stable.
user_*.md— a single user-profile memory. If it's short and mostly
facts (role, preferences, expertise), you'll save <200 tokens and the YAML overhead eats most of that. Compress only if the file is large and rule-heavy.
Skipped by default
MEMORY.md— the auto-memory index. Already one-liners; nothing to compress.project_*.md— Claude auto-memory "project" type. These are narrative
scratchpads with SHAs, version tags, TODO markers ("PICK UP HERE", "LANDED"), file paths, and mid-task edits. They decay fast (many are archived or deleted within days). Compressing then discarding is wasted effort, and the closed schema fights their loose narrative shape.
Override
If you know what you're doing and want to compress everything Terse-MD found, pass --include-all to either command:
/terse-md:analyze ~/.claude/projects/-mnt-data-game2/memory --include-all
/terse-md:run --all ~/.claude/projects/-mnt-data-game2/memory --include-allNaming a single file explicitly with /terse-md:run <path> always bypasses the default-skip filter — if you point Terse-MD at a specific file, it trusts you.
The honest caveats
Read this before you run /terse-md:run on anything you care about.
1. The approval step is the entire safety model. Don't click through.
Terse-MD's promise of "lossless by meaning" is not a mechanical guarantee — it's a human-approved guarantee. The tool compresses, then decompresses, then shows you the original next to the reconstruction and asks if meaning survived. If you click Approve without reading the diff, you have bypassed the only thing that keeps drift out of your compressed file.
This matters because Claude will follow the drifted version on every future session. If the reconstruction dropped a rationale, a path, a "why we do X", that context is now gone from what Claude sees. You may not notice until Claude starts making choices you thought it wouldn't.
If you're tempted to approve reflexively because the diff is long: that's the exact moment the tool is asking you to do its most important job. Reject and edit the source to fit the schema better, or don't compress that file.
2. Compression ratios vary more than "50–70%" suggests.
That number applies to rule-heavy CLAUDE.md-shaped files. Real ranges I've seen in testing:
| Content shape | Typical savings | |---------------------------------------------|-----------------| | CLAUDE.md with many behavioural rules | 50–70% | | feedback memory (rule + why + how-to-apply) | 40–60% | | Short reference memory (<500 tokens) | 15–25% | | Dense reference with inline commands | 20–30% with meaningful context loss | | Very short files (<200 tokens) | near zero or negative — YAML overhead |
3. The compression pipeline is not free.
Each file burns ~60k tokens of subagent context (3 Sonnet calls: normalize, compress, decompress). A file has to be re-read many times to amortize that cost. CLAUDE.md easily does (read every session × hundreds of sessions). Individual feedback memories probably don't. Run /terse-md:analyze first and think about re-read frequency before compressing a whole directory.
4. The closed schema is opinionated.
The schema has rules, transforms, triggers, thresholds, and meta — five sections chosen for behavioural instructions. Reference material (facts, paths, URLs, "here's the state of the world") fits awkwardly. When in doubt, either: (a) rewrite the source into imperative rule shape before compressing, or (b) don't compress that file.
How it works
Three Sonnet subagents run in sequence. The first rewrites your prose in canonical form, stripping filler without altering meaning. The second emits YAML conforming to schema.yaml — a closed vocabulary of rules,
transforms, triggers, thresholds, and meta. Any key outside the schema fails validation, and the compress step is retried once with the error attached. The third expands the validated YAML back to prose. You review source-vs-reconstruction side-by-side and approve only if meaning survived. The approved YAML is written next to the source as
<name>.approved.yaml — that file is what Claude loads on future sessions.
What it's not
- Not a token-optimizer that guarantees savings (it measures, then asks).
- Not lossless at the wording level (it's lossless at the meaning level — you approve the diff).
- Not affiliated with Anthropic.
License
MIT. See LICENSE.
