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 lync-cyber/CataForge --skill framework-issue-resolvegit clone --depth 1 https://github.com/lync-cyber/CataForgeWrote 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/lync-cyber/cataforge/framework-issue-resolve)<a href="https://agentmods.dev/skills/lync-cyber/cataforge/framework-issue-resolve"><img src="https://agentmods.dev/badge/skills/lync-cyber/cataforge/framework-issue-resolve/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/lync-cyber/cataforge/framework-issue-resolve"><img src="https://agentmods.dev/badge/skills/lync-cyber/cataforge/framework-issue-resolve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00084 | $0.02367 |
| Opus 5.5 | $0.00034 | $0.00947 |
| Sonnet 5.5 | $0.00017 | $0.00473 |
| Haiku 4.5 | $0.00008 | $0.00237 |
Grade A, and why
framework-issue-resolve 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
框架 issue 闭环消化 (framework-issue-resolve)
五步闭环
1. 拉取 (fetch) → cataforge issue triage [--dry-run]
2. 审查分析 (analyze) → draft 落 docs/reviews/triage/SKILL-IMPROVE-<id>-issue-<N>.md
3. 给修复意见 (propose)→ draft 内 verdict + rationale + 修复路径
4. 实施 (implement) → feature branch + Edit + commit + PR(标准 dev 流程)
5. 关闭 (close) → cataforge issue close <N> --verdict <v> [--pr <P>] [--reason ...]
每步之间是 maintainer 强制 checkpoint:自动化只跨 1↔2 和 5;3↔4 是人工 go/no-go。
能力边界
- 能做: 拉取 + Layer 1 字段解析 + 草稿渲染 + close 模板化(统一文案)
- 不做: Layer 2 语义分析(仍是基于正则的字段抽取);自动跨过 maintainer 实施 PR;自动改 issue label(用户/maintainer 手动)
审查与修复纪律
Step 2–4 定位与修复前按此自检,规避 LLM 常见误区(「做 A 而非 B」):
- 先在当前代码复现,再判根因:用最小路径验证症状在当前版本仍可复现,而非凭 issue 描述或
confirmedverdict 直接动手 —— verdict 是 Layer 1 事实核查,不代表根因已定。 - 先查是否已实现:动手前搜索目标逻辑是否已存在(可能在另一层或另一入口未接通),而非假设缺失即新写。
- 修根因而非症状:沿调用链 / 数据流定位最深层原因(如 SSOT 违背、生产方与消费方解析不对称),改产生缺陷的一方,而非让消费方容忍坏输入。
- 验证 reporter 建议的前提:其修复方向可能机制不可行、已被实现或仅触及症状;先核实再决定采纳 / 替代 / 否决,不照搬。
- 核全部入口:某能力已存在却仍报缺陷时,确认所有调用路径都接通它,而非只改单一入口。
- 修复设计以收敛为准绳:修复应减少而非增加同一语义在系统中的实现数——不变量只保留一个实现点,置于全部路径必经的最窄处;复用既有机制与信号,删除无生产者 / 无消费者的路径;而非在各入口分别贴补丁、另造检测副本或为死路径保留兼容。
- 可复现缺陷先写失败测试:先写钉住症状的失败测试(RED)再修(GREEN),把回归固化。
输入规范
framework.json#upgrade.source.repo— 拉取目标framework.json#feedback.gh.labels— 默认 label 过滤集合- 本地
.cataforge/skills/*和.cataforge/agents/*— id 核对 cataforge.__version__— reported_version vs installed_version 比对
输出规范
Step 2 草稿 frontmatter
---
id: skill-improve-<target_id>-issue-<N>
doc_type: skill-improve
status: draft
source_issue: <N>
source_url: https://github.com/<owner>/<repo>/issues/<N>
reported_version: <X.Y.Z>
installed_version: <cataforge.__version__>
verdict: <见下表>
target_id: <skill/agent id, 或 source 文件名>
target_kind: skill | agent | source | schema | mixed | aggregate
target_path: <可选>
rationale: "..."
---
verdict 五态
| verdict | 触发 | 后续动作 |
|---|---|---|
confirmed |
引用了存在的 skill/agent/源文件 + reporter version 持平 | 写草稿;步骤 4 实施修复;步骤 5 close --verdict fixed |
wontfix-by-design |
maintainer 判定下游误解了主动设计(如带点 doc_id 被框架主动拒绝) | 草稿写出主动设计的 evidence;步骤 5 close --verdict wontfix --reason ... |
already-fixed |
reported_version < installed_version(自动判定) | 不写草稿;步骤 5 close --verdict already-fixed --pr <历史 PR> |
needs-repro |
body 缺 cataforge --version 行 |
不写草稿;评论要求 reporter 重跑 cataforge feedback bug --gh,加 label needs-repro(手动) |
unrelated |
不像 feedback bundle | 不写草稿;可手动 close 为 off-topic |
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 · 159 lines · 84 tokens per session scan A 81ee7d09ba95
framework-issue-resolve is a skill published in the GitHub repository lync-cyber/CataForge (128 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 2,367 once invoked, about $0.0003 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.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.
ci-fixer
CI failures - read error, minimal fix, verify.