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 tahirraufkeeyu/software-development-agent-stack--sdas --skill vulnerability-remediationgit clone --depth 1 https://github.com/tahirraufkeeyu/software-development-agent-stack--sdasWrote 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/tahirraufkeeyu/software-development-agent-stack--sdas/vulnerability-remediation)<a href="https://agentmods.dev/skills/tahirraufkeeyu/software-development-agent-stack--sdas/vulnerability-remediation"><img src="https://agentmods.dev/badge/skills/tahirraufkeeyu/software-development-agent-stack--sdas/vulnerability-remediation/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/tahirraufkeeyu/software-development-agent-stack--sdas/vulnerability-remediation"><img src="https://agentmods.dev/badge/skills/tahirraufkeeyu/software-development-agent-stack--sdas/vulnerability-remediation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00075 | $0.03479 |
| Opus 5 | $0.00037 | $0.01740 |
| Sonnet 5 | $0.00015 | $0.00696 |
| Haiku 4.5 | $0.00007 | $0.00348 |
Grade A, and why
vulnerability-remediation 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 7d 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 — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
security-audithas producedsecurity/findings/audit.jsonwith SAST and/or DAST findings.- A Semgrep / SonarQube / ZAP / Nuclei scan from CI has failed the pipeline on a Medium+ finding.
- A pen-test report has logged a code-level vulnerability that needs engineering-owned remediation.
- A threat-modeling exercise identified an unmitigated class of risk and you need to patch its current instances.
Do not use this skill for dependency CVEs (use dependency-remediation) or container-image issues (use container-remediation). Do not use it for architectural redesign — that's a design-level decision, not a remediation.
Inputs
security/findings/audit.jsonwith category-labelled findings.- Source repository checkout with test infrastructure runnable locally.
- The exact scanner rule ID per finding (needed to re-verify closure).
Outputs
security/remediation/vulnerabilities-<date>.md— per-finding audit trail.- Source-code patches with clear before/after.
- One regression test per finding (unit, integration, or API contract, depending on category).
- A PR with CVE-style findings closed + test evidence + re-scan result.
Tool dependencies
- SAST:
semgrep(p/owasp-top-ten,p/sql-injection, custom rule packs),sonar-scanner. - DAST:
zap-baseline.py,nuclei,wapiti. - Language-specific lint:
bandit(Python),gosec(Go),eslint-plugin-security(JS),brakeman(Ruby). - Test runners:
pytest,jest/vitest,go test,rspec.
Procedure
-
Triage by category. Bucket each finding into one of the canonical classes:
Category OWASP ref Example rules Injection A03 sql-injection,command-injection,ldap-injection,sstiBroken access control A01 missing-authz-check,idor,path-traversal,unrestricted-file-uploadXSS A03 reflected-xss,stored-xss,dom-xssCryptographic / auth A02 weak-hash,predictable-randomness,insecure-jwt,missing-csrfSecurity misconfiguration A05 missing-security-headers,verbose-errors,cors-wildcard-credentialsSensitive data exposure A02 password-in-logs,stack-trace-in-response,pii-in-urlSSRF A10 ssrf-user-controlled-urlInsecure deserialization A08 pickle-load,readObject-without-filter
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.
- 7d ago First seen · 381 lines · 75 tokens per session scan A d97b7338135c
vulnerability-remediation is a skill published in the GitHub repository tahirraufkeeyu/software-development-agent-stack--sdas (18 stars, last pushed 4mo ago), licensed MIT. It adds 75 tokens to every session and 3,479 once invoked, about $0.0004 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 2026-09-03.
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