Chunk review
Skill mnox/mnox-ai/plugins/config-chunks/skills/chunk-review
AI utils & skills
npx -y skills add mnox/mnox-ai --skill chunk-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 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.
What its author says it does
Copied from the file, not written here
Score a candidate guidance chunk for inclusion in the always-on agent-instruction bundle (the concatenated context injected into every agent session). Use when reviewing or authoring a chunk before opening a chunk PR, or when asked to "/chunk-review", "score this chunk", "review a guidance chunk", "review this chunk", "is this chunk worth shipping", "should this go in the bundle", or "is this chunk-worthy". Applies a 7-axis rubric (Universality, Altitude, Not-a-skill, Non-redundancy, Concision, Impact, Stability), enforces hard size gates, checks for redundancy against existing chunks, and emits a score plus a keep / revise / cut verdict.
SKILL.md
12.4 KB, as published. Nobody here has run it
chunk-review
Overview
A guidance chunk is a block of agent instruction that gets concatenated into an always-on context bundle — injected into every agent session for every subscriber, in every provider that imports the bundle, forever. That is expensive, permanent real estate: it costs context tokens on every turn and it competes for the model's attention with every other chunk. This skill is the quality gate that keeps the bundle lean, high-signal, and genuinely behavior-changing.
Use it to score a candidate chunk, assign a verdict, and check it against existing chunks for redundancy before a chunk PR is opened.
The 7 axes split into two families:
- Does it belong here? — Universality, Altitude, Not-a-skill, Non-redundancy.
- Does it earn its tokens? — Concision, Impact, Stability. (These three are the always-on-context tax filter: a chunk can be perfectly on-topic and still be noise if it changes no behavior, churns constantly, or is bloated.)
Input
A chunk file (template-conformant — frontmatter + body). Either a path, a paste, or a diff. If reviewing an authored-but-unsaved chunk, score the proposed content.
Step 1 — Validate the frontmatter
The chunk MUST have all required keys. Reject outright (score 0, verdict
cut) if any are missing or malformed:
name— unique, kebab-case (the dedup key)version— semverowner— the owning plugin slugorder— integer sort keysummary— one line
Optional progressive-disclosure keys:
disclosure—inline(default) orpointer. Apointerchunk MUST also setskill:. Its body should be a 1–3 line rule only; the heavy how-to lives in the named skill (loaded on demand, not in always-on context).skill— required iffdisclosure: pointer. Kebab-case skill slug. If the named skill isn't installed or shipped alongside this chunk, flag it in the recommendation — a pointer to a skill nobody has is dead context.
Step 1b — Size gate (hard fail / auto-fail)
Strip the frontmatter and measure the body in characters
(awk 'BEGIN{c=0} c<2 && /^---[[:space:]]*$/{c++; next} c>=2{print}' file | wc -c).
Hard caps (chunks above these → auto-fail, score 0, verdict cut, recommend split):
| disclosure | cap (chars) | rationale |
|---|---|---|
inline (default) | 2000 | ~500 tokens of always-on context tax in every session for every subscriber. |
pointer | 400 | A pointer chunk is supposed to be a stub; anything bigger means the rule has crept into the always-on path. |
A chunk in the 75–100% band of its cap is borderline — dock the Concision axis and recommend trimming or splitting before merge.
Step 2 — Score the rubric (0–70)
Seven axes, 0–10 points each, summed to a total out of 70. Score each axis on its own evidence; do not average them. Use the anchors below.
Axis 1 — Universality
Is this useful to every subscriber, regardless of team, project, or stack?
- 10 — Genuinely applies in every session for every subscriber.
- 5 — Useful to a broad slice, irrelevant to a meaningful minority.
- 0 — Niche; matters to one team, one repo, or one workflow.
Axis 2 — Altitude
Does it belong at the always-on / global level, vs. a project-local file or a task?
- 10 — Cross-cutting policy or convention that should shape work everywhere.
- 5 — Mostly global but leaks project- or stack-specific detail.
- 0 — Belongs in a project-local instructions file, or is really a one-off task.
Axis 3 — Not-a-skill
Is it always-on guidance, vs. an on-demand procedure?
- 10 — Must passively shape every response; nothing to "invoke."
- 5 — Mostly a posture, but carries procedural how-to that could be deferred.
- 0 — Procedural / step-by-step; should be a skill, loaded when needed.
Axis 4 — Non-redundancy
Does it add something platform defaults and existing chunks don't already cover?
- 10 — Novel; no existing chunk or platform default says this.
- 5 — Partial overlap with an existing chunk; could merge or re-scope.
- 0 — Restates a platform default or duplicates an existing chunk (see Step 3).
Axis 5 — Concision
Does every line earn its tokens?
- 10 — Tight, bounded, no padding; well under its size cap.
- 5 — Serviceable but loose; or in the 75–100% cap band.
- 0 — Rambling, padded, or unbounded; context cost outweighs value.
Axis 6 — Impact (behavioral leverage)
Would omitting this chunk visibly degrade agent behavior — or is it low-leverage noise the model already does, ignores in practice, or treats as decoration?
High-signal context engineering means finding the smallest set of high-signal tokens that maximize the likelihood of the desired behavior; instructions change behavior only when they give the model concrete signals for a desired output, not vague restatements of the obvious. A chunk with no behavioral leverage is pure tax — it dilutes the signal-to-noise ratio of the whole bundle.
- 10 — Directive and consequential. Removing it would produce visibly worse output (wrong default, skipped check, missed convention). Gives a concrete, actionable signal — a real "do/don't" with teeth.
- 5 — Plausibly helpful but soft. Nudges tone or preference; hard to point at a behavior that would change if it were deleted.
- 0 — Decorative, obvious, or ignored-in-practice. States what the model already does by default, is too vague to act on, or is aspirational fluff.
Heuristic: write the one-sentence regression you'd expect if this chunk were deleted. If you can't name a concrete behavioral regression, it's a 0–3.
Axis 7 — Stability (durability / low-churn)
Does this encode a durable invariant, or a volatile specific that will rot?
Always-on context should carry durable principles and heuristics, not volatile facts. Specifics — names, versions, dates, current-sprint details, tool flags, people, URLs — go stale silently and cause drift: the bundle keeps asserting something that is no longer true, and the model follows stale guidance with full confidence. Durable guidance sits at the "right altitude": specific enough to steer behavior, general enough to survive change without edits.
- 10 — Durable invariant or principle. Would still be correct a year from now with no edits. No version numbers, dates, or named transient specifics.
- 5 — Mostly durable but pinned to something semi-stable (a tool name, a current convention) that may shift within a year.
- 0 — Volatile detail with a short shelf life (versions, dates, sprint/quarter specifics, a current person/owner, a soon-to-change flag). Will rot and mislead.
Heuristic: ask "what has to change in the world for this line to become a lie?" If the answer is "a routine, frequent event," it's a 0–3 — push the volatile part out to a skill, a project-local file, or a lookup, and keep only the durable rule here.
Step 3 — Similarity check
Compare the candidate against every existing chunk in the bundle's chunks/
sources:
- Overlapping
name→ hard conflict (same dedup key; one silently loses). The PR must rename or supersede. - Chunks covering the same subject → recommend merge, a clear
orderseparation, or dropping the weaker one. - Quote the overlapping lines so the author sees the redundancy.
Feed the result into the Non-redundancy axis.
Step 3b — Disclosure fit
Pointer chunks deserve their own check:
- The body MUST be a rule (imperative, always-applies), not a procedure. If it reads like "here's how to do X," the procedure belongs entirely in the referenced skill and the chunk shouldn't exist inline.
- The
skill:slug must resolve to a skill the subscriber will actually have installed (same plugin, or a documented dependency). - If an inline chunk is over the 75% size band AND its procedural detail could
auto-load via a skill description, recommend conversion to
pointerwith a sibling skill. This also lifts its Concision and Impact scores.
Step 4 — Verdict
Total the seven axes (out of 70), then map to a verdict. Auto-fail conditions
(missing/malformed frontmatter, or body over its hard size cap) force cut
regardless of any partial scoring.
| Verdict | Band | Meaning |
|---|---|---|
keep | ≥ 49 / 70 (~70%), no hard conflict | Chunk-worthy. Ship it. |
revise | 28–48 / 70 (~40–69%) | Right idea, wrong shape. Fix the weak axes (often the wrong vehicle, redundancy, low Impact, or volatile content) and re-score. Includes "move elsewhere" — project-local file, a skill, a plugin README, or team docs. |
cut | < 28 / 70 (~<40%), or any auto-fail | Should not exist as a chunk. |
Additional hard gates that cap the verdict at revise even on a high total:
- Impact ≤ 2 — low behavioral leverage means it's tax, not signal. Don't keep it until you can name the regression its absence would cause.
- Stability ≤ 2 — volatile content will rot the bundle. Don't keep it until the volatile part is pushed out and only the durable rule remains.
Output format
## chunk-review: <name> v<version> · disclosure=<inline|pointer>
**Score: <0-70>** · **Verdict: keep | revise | cut**
**Body size:** <chars> / <cap> (<inline|pointer>)
| Axis | Score | Note |
|------|-------|------|
| Universality | x/10 | ... |
| Altitude | x/10 | ... |
| Not-a-skill | x/10 | ... |
| Non-redundancy | x/10 | ... |
| Concision | x/10 | ... |
| Impact | x/10 | ... |
| Stability | x/10 | ... |
| **Total** | **x/70** | |
**Similarity check:** <conflicts / overlaps found, or "clear">
**Disclosure fit:** <pointer-ok / pointer-skill-missing / consider-pointer / n/a inline-ok>
**Deletion regression (Impact):** <one sentence: what gets visibly worse without this chunk, or "none — low leverage">
**Rot risk (Stability):** <what would make this line a lie, or "none — durable">
**Recommendation:** <one paragraph — if not `keep`, name the fix or the right home>
Be blunt. The bundle is shared and permanent; a soft review here is a tax on every subscriber, in every session, forever.
Grounding
The two newest axes are grounded in current context-engineering practice:
-
Impact operationalizes the signal-to-noise principle: context is finite and precious, so the goal is the smallest set of high-signal tokens that change behavior — instructions only steer the model when they give concrete signals for a desired output, not vague or obvious restatements. Adding low-leverage text actively worsens output by diluting signal and burying real instructions in noise. (Anthropic, Effective context engineering for AI agents; Prompt Engineering Guide, Context Engineering.)
-
Stability operationalizes durability vs. brittleness: always-on artifacts should encode durable instructions at the "right altitude" — specific enough to guide behavior, flexible enough to provide strong heuristics — and avoid brittle, hardcoded specifics that create fragility and maintenance burden. Volatile detail causes context rot and version drift, where the bundle keeps asserting stale facts the model then follows confidently. (Anthropic, Effective context engineering for AI agents; Mizrahi et al., State of What Art? A Call for Multi-Prompt LLM Evaluation, on prompt brittleness.)
Sources:
- Anthropic — Effective context engineering for AI agents: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
- Prompt Engineering Guide — Context Engineering: https://www.promptingguide.ai/agents/context-engineering
- Mizrahi et al. — State of What Art? A Call for Multi-Prompt LLM Evaluation (arXiv 2401.00595): https://arxiv.org/html/2401.00595v3