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 kanyun-inc/reskill --skill code-confidence-mapgit clone --depth 1 https://github.com/kanyun-inc/reskillWrote 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/kanyun-inc/reskill/code-confidence-map)<a href="https://agentmods.dev/skills/kanyun-inc/reskill/code-confidence-map"><img src="https://agentmods.dev/badge/skills/kanyun-inc/reskill/code-confidence-map/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/kanyun-inc/reskill/code-confidence-map"><img src="https://agentmods.dev/badge/skills/kanyun-inc/reskill/code-confidence-map.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 135 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00104 | $0.03455 |
| Opus 5.5 | $0.00042 | $0.01382 |
| Sonnet 5.5 | $0.00021 | $0.00691 |
| Haiku 4.5 | $0.00010 | $0.00346 |
Grade A, and why
code-confidence-map 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 1mo 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Confidence Map
Helps AI agents answer a core question: "If this code breaks tomorrow, how well-equipped are we to understand and fix it?"
This is NOT a linter or code quality tool. It measures comprehensibility risk — the gap between code complexity and human understanding. The AI's unique advantage over static analysis tools is semantic judgment: it can tell that processData does 5 unrelated things, that tests only cover happy paths, or that // don't touch this is a red flag.
When to Use This Skill
Use this skill when the user:
- Asks about code confidence, risk, or maintainability
- Says "is this code safe to change?" or "how risky is this module?"
- Is onboarding to a new codebase and wants to understand the landscape
- Wants to know where the tech debt or weak spots are
- Asks "which parts of the codebase are well-tested?"
- Says "scan this directory" or "check this file's health"
- Is planning a refactor and wants to assess risk first
- Asks about code quality, code health, or how solid a module is
Core Concept: What "Confidence" Means
Confidence is the answer to: "If something goes wrong here, can we understand and fix it?"
It is NOT:
- A judgment on code quality (LOW confidence is a risk signal, not a verdict)
- A measure of cleverness or elegance
- An indicator of who wrote the code or when
Confidence levels:
| Level | Meaning |
|---|---|
| HIGH | Well-tested, readable, good error handling — safe to work with |
| MEDIUM | Partially covered — some gaps in tests, docs, or error handling |
| LOW | Significant gaps — risky to modify without additional preparation |
Do NOT use percentages (e.g., "73.2%"). They imply false precision for what is fundamentally a judgment call.
Overall level rule: The overall confidence level equals the lowest dimension level. If any single dimension is LOW, the overall is LOW — one critical gap is enough to make code risky to work with.
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.
- 1mo ago First seen · 349 lines · 104 tokens per session scan A 1532c90f5bce
code-confidence-map is a skill published in the GitHub repository kanyun-inc/reskill (60 stars, last pushed 1mo ago), licensed MIT. It adds 104 tokens to every session and 3,455 once invoked, about $0.0004 per session on Opus 5.5. 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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