Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add samber/cc-skills --skill skill-progressive-disclosure-designgit clone --depth 1 https://github.com/samber/cc-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/samber/cc-skills/skill-progressive-disclosure-design)<a href="https://agentmods.dev/skills/samber/cc-skills/skill-progressive-disclosure-design"><img src="https://agentmods.dev/badge/skills/samber/cc-skills/skill-progressive-disclosure-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samber/cc-skills/skill-progressive-disclosure-design"><img src="https://agentmods.dev/badge/skills/samber/cc-skills/skill-progressive-disclosure-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00160 | $0.03607 |
| Opus 5 | $0.00080 | $0.01803 |
| Sonnet 5 | $0.00032 | $0.00721 |
| Haiku 4.5 | $0.00016 | $0.00361 |
Grade A, and why
skill-progressive-disclosure-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Progressive Disclosure Design
Each section that recommends a direction includes explicit pros and cons. The decisions in this skill are trade-offs, not rules. The model using this skill should reason from the trade-offs to the user's specific situation rather than apply rules blindly.
Triggering vs. disclosure: separate these first
Two problems get conflated and need separating before any splitting decision.
Triggering is whether Claude invokes the skill at all. Driven entirely by the YAML description. File splitting does not affect triggering. If the question is "my skill doesn't trigger reliably", do not split files, fix the description (use run_loop.py from the skill-creator skill).
Progressive disclosure is what loads after the skill activates. SKILL.md body always loads. references/* only loads when SKILL.md tells the model to read a specific file. scripts/* executes without loading into context at all. This is where context protection happens.
If the user is asking about splitting because of triggering issues, surface the confusion first and redirect.
Default: do not split
A monolithic SKILL.md beats a split one until proven otherwise.
Split only when at least one is true:
- SKILL.md exceeds ~400 lines and content has natural branches.
- Empirical evidence (eval transcripts) shows the model wasting context on irrelevant sections.
- Specific content is large and only needed in narrow conditions.
Pros of staying monolithic:
- Single context load, no router prose to maintain.
- No tool-call overhead from reading references.
- No risk of the model loading the wrong reference or skipping a needed one.
- Easier to maintain: one file, one source of truth.
- Better for highly interconnected content where context is global.
- Easier for human reviewers to read end-to-end.
Cons of staying monolithic:
- Every invocation pays the full token cost, even when only 10% of the content is relevant.
- Does not scale past ~500 lines without degrading the model's ability to find what matters.
- No mechanism to gate rare or niche content.
- All content must justify its always-loaded status.
- Maintenance gets harder as the file grows.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed 3184d1f636e2
- 11d ago First seen · 355 lines · 160 tokens per session scan A 3fd4d0da0006
skill-progressive-disclosure-design is a skill published in the GitHub repository samber/cc-skills (209 stars, last pushed 4d ago), licensed MIT. It adds 160 tokens to every session and 3,607 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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