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 JuliusBrussee/skills --skill last-20-percentgit clone --depth 1 https://github.com/JuliusBrussee/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/juliusbrussee/skills/last-20-percent)<a href="https://agentmods.dev/skills/juliusbrussee/skills/last-20-percent"><img src="https://agentmods.dev/badge/skills/juliusbrussee/skills/last-20-percent/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/juliusbrussee/skills/last-20-percent"><img src="https://agentmods.dev/badge/skills/juliusbrussee/skills/last-20-percent.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 | $0.00123 | $0.01852 |
| Opus 5 | $0.00062 | $0.00926 |
| Sonnet 5 | $0.00025 | $0.00370 |
| Haiku 4.5 | $0.00012 | $0.00185 |
Grade A, and why
last-20-percent 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- last-20-percent — 98% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Last 20%
Agents reliably deliver the first 80% of a solution — the ingestion, the endpoints, the schema, the pipeline — and quietly skip the 20% that makes it something a person would love. Asked for an enterprise knowledge wiki, an agent builds the crawler, the embeddings, the LLM synthesis layer, and never once asks what a wiki page should read like, which pages should exist, or how someone finds an answer on day one. The result compiles, passes tests, demos fine, and is dead on arrival.
This skill exists to find that 20%, spec it as concretely as the plumbing, and finish it.
Why the 20% gets skipped
The failure is structural, so the countermeasures must be too:
- Tasks come from the noun. Decomposing "wiki" yields ingestion, storage, search, generation. The experience of using the wiki never appears in that decomposition — so it never becomes a task, so it never gets done.
- "Done" is defined by what's checkable. A test can assert a 200 response. It cannot assert "this page answers the question in its first paragraph." Work without done-criteria silently drops off the plan.
- Taste feels like overreach. Deciding what a page should read like is a product judgment. Models hedge: they build "flexible" machinery and defer every real decision to a user who asked precisely because they wanted those decisions made.
- The 20% is scheduled last, and last gets cut. Even when the experiential work is named, it lands at the end of the plan — exactly where context, budget, and attention run out.
Each phase below counters one of these.
The cardinal rule
Never call a solution done until you have consumed its output the way the end user will — and it delivered the magic moment. Reading your own code is not consuming the output. Passing tests is not consuming the output. Open the wiki and read three pages cold. Run the report and read the report. Load the dashboard and try to answer a real question with it.
Phase 1: Write the magic moment
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.
- 12d ago First seen · 93 lines · 123 tokens per session scan A 3eceb2337621
last-20-percent is a skill published in the GitHub repository JuliusBrussee/skills (162 stars, last pushed 1mo ago), licensed MIT. It adds 123 tokens to every session and 1,852 once invoked, about $0.0006 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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