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 agentmods add skills/pangon/ai-sdlc-framework/sdlc-fixnpx skills add pangon/ai-sdlc-framework --skill sdlc-fixgit clone --depth 1 https://github.com/pangon/ai-sdlc-frameworkWrote 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/pangon/ai-sdlc-framework/sdlc-fix)<a href="https://agentmods.dev/skills/pangon/ai-sdlc-framework/sdlc-fix"><img src="https://agentmods.dev/badge/skills/pangon/ai-sdlc-framework/sdlc-fix.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00061 | $0.04723 |
| Opus 5 | $0.00030 | $0.02361 |
| Sonnet 5 | $0.00012 | $0.00945 |
| Haiku 4.5 | $0.00006 | $0.00472 |
Grade B, and why
SDLC-fix scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- **External tool and library dependencies**: when a fix requires tools or libraries that are not available in the current environment (system packages like ffmpeg, database engines, runtime tools, etc.), **always notify How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
You are applying a user-reported fix, bug correction, or modification to the codebase. The work is not driven by tasks.md — the user describes what needs to be done and you derive the scope, context, and implementation plan from that description combined with the project's authoritative sources.
Phase Validation
Before doing anything else, read the **Phase**: field of the ### Current State subsection in CLAUDE.md. Then follow the matching case below:
-
Not initialized— Stop, recommend/SDLC-init, and do not proceed. -
Specification— Stop, recommend/SDLC-elicitto continue refining specification or/SDLC-designto start the design phase, and do not proceed. -
Design— Stop, recommend completing design with/SDLC-design, then/SDLC-decomposeand/SDLC-implementation-planto create the task list, and do not proceed. -
Code— this is the expected state. Proceed with Issue Elicitation.
Issue Elicitation
Gather enough context from the user to understand the problem and determine the scope of the fix. Ask the user the following, adapting the questions to what they have already provided:
-
Problem description — What is the issue, bug, or desired change? Ask for:
- Observed behavior vs. expected behavior (for bugs)
- What they want to achieve (for changes/enhancements)
- Steps to reproduce (for bugs, if applicable)
- Error messages or logs (if available)
-
Affected area — Which part(s) of the system are involved? Help the user identify:
- Which component(s) are affected (e.g., frontend, backend) — refer to the components listed in
3-code/CLAUDE.code.md - Which scripts or runbooks are involved (if the fix targets deploy/operations artifacts in
4-deploy/) - Which specific files, modules, or features they believe are involved (if they know)
- Which component(s) are affected (e.g., frontend, backend) — refer to the components listed in
-
Scope boundaries — Clarify what is in scope and what is not:
- Is this a minimal targeted fix, or should related issues in the same area also be addressed?
- Are there any constraints on the approach (e.g., "don't change the API contract", "must be backwards compatible")?
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 First seen · 236 lines · 61 tokens per session scan B b971e86fca8d
SDLC-fix is a skill published in the GitHub repository pangon/ai-sdlc-framework (125 stars, last pushed 13d ago), licensed Apache-2.0. It adds 61 tokens to every session and 4,723 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). 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.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
azsdk-common-pipeline-analysis
Analyze Azure SDK CI/CD pipeline failures into a structured diagnosis, and define the required output format. Load this skill before calling azsdkanalyzepipeline, which returns raw failure data that this skill interprets and formats. USE FOR: "pipeline failed", "build failure", "CI check failing", "tests failing in…
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
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…