Applies NIST/CWE security hardening to Python and Rust code. Use when auditing code for vulnerabilities or proposing concrete security remediations.
npx skills add https://github.com/athola/claude-night-market --skill harden
Active security hardening: scan the existing repository for
vulnerabilities and forward-facing threats, then propose concrete
remediations the user can approve, defer, or file.
This skill is the engine behind /harden. It complements the
Claude Code built-in /security-review (which scans the pending
diff) by sweeping the whole repository against citation-backed
checks rather than line-level review of in-flight code.
/security-review.attune:war-roomwith a security-focused panel.
but does not propose protocol fixes (specialist work).
pensive:bug-review.harden:discovery: inventory languages, build files, hooks,CI workflows
harden:scan-python: run python-checks.md detectors whenPython is present
harden:scan-rust: run rust-checks.md detectors when Rustis present
harden:scan-cross-cutting: run cross-cutting.md detectors(deps, secrets, SBOM, CI)
harden:scan-frontier: run frontier-checks.md (PQC, LLMsupply chain, sandboxing)
harden:nist-mapping: map findings to NIST SSDF practicesharden:proposals: for each finding above the threshold,draft a concrete remediation per modules/proposal-shape.md
harden:approval-gate: present proposals to the user forapply / file / defer / reject
harden:apply-and-validate: apply approved proposals asdiscrete commits, re-run gates, capture evidence
10. harden:findings-verified: citations confirmed by
citation_verifier.py
11. harden:report: write reviews/harden-<date>.md and
optionally post to Discussions
Load modules based on what the discovery step finds.
| Detected | Load |
|----------|------|
| Python files (*.py, pyproject.toml) | modules/python-checks.md |
| Rust files (*.rs, Cargo.toml) | modules/rust-checks.md |
| Any | modules/nist-controls.md (citation backbone) |
| Any | modules/cross-cutting.md (deps, secrets, CI) |
| LLM SDK use (anthropic, openai), MCP server, post-quantum surface | modules/frontier-checks.md |
| Any with proposals enabled | modules/proposal-shape.md |
The module hub keeps the SKILL.md itself under the
estimated_tokens: 1100 budget. Detail lives in the modules.
Inventory the repo without modifying anything:
# Languages and build files
find . -type f \( -name '*.py' -o -name '*.rs' -o -name '*.sh' \) \
| head -200 > /tmp/harden-langs.txt
# Build manifests
ls pyproject.toml Cargo.toml package.json go.mod 2>/dev/null
# CI workflows and pre-commit
ls .github/workflows/ .pre-commit-config.yaml 2>/dev/null
# Hooks and Dockerfiles
find . -path ./node_modules -prune -o -type f \
\( -name 'hooks.json' -o -name 'Dockerfile*' \) -print
Dispatch /discovery-prefilter if the repo has > 5000 source files
to bound the scan.
For each detected language, load the matching module and run its
detector list. Each detector outputs findings with the schema
defined in modules/proposal-shape.md. The citation column is
mandatory: a finding without a NIST/CWE reference is downgraded
to "advisory" and not eligible for active proposal.
Group findings by SSDF practice (PW.4, PW.8, RV.1, etc.) and CWE
ID. The mapping table lives in modules/nist-controls.md. The
report's executive summary references SSDF practice coverage so
the audit is comparable across runs.
For each finding above the configured severity threshold, draft a
concrete remediation per modules/proposal-shape.md:
pensive:blast-radiusPresent proposals one at a time via AskUserQuestion. Default
options: apply, file as issue, defer to backlog,
reject. Auto-apply is opt-in via the --auto-apply flag and
respects a per-finding severity threshold.
Apply each approved proposal as a discrete commit:
git add <touched files>
git commit -m "harden: <finding-id> <one-line summary>"
After each apply, re-run the project gates:
make test --quiet && make lint && make type-check
If a gate fails, revert the commit (git revert HEAD --no-edit)
and downgrade the finding to "needs human design."
Write reviews/harden-<date>.md with:
If running inside a PR context, post the executive summary as a
comment via abstract:post_review_insights.
| Severity | Definition | Default disposition |
|----------|------------|---------------------|
| CRITICAL | Active exploit path, RCE, credential leak | apply or file immediately |
| HIGH | Plausible exploit, missing defense-in-depth on attack surface | propose for apply |
| MEDIUM | Best-practice gap, hardening opportunity | propose for apply with --auto-apply medium |
| LOW | Style/documentation gap with security flavor | file as issue |
| ADVISORY | Pattern detected without exploit narrative | report only |
# Hardening Report — <date>
## Executive Summary
- Codebase: <repo> @ <sha>
- Languages scanned: Python (X files), Rust (Y files)
- NIST SSDF practices covered: PW.4, PW.7, PW.8, RV.1, RV.2
- CWE Top 25 hits: <count> across <distinct CWEs>
- Disposition: <N> applied, <N> filed, <N> deferred, <N> rejected
## Findings
| ID | Severity | Citation | File:Line | Disposition |
|----|----------|----------|-----------|-------------|
| H1 | CRITICAL | CWE-502, NIST SSDF PW.7 | `src/x.py:45` | applied (commit abc123) |
| H2 | HIGH | CWE-89, NIST SSDF PW.4 | `src/y.py:120` | filed (#456) |
## Per-finding detail
### H1 — Unsafe deserialization
**Citation:** CWE-502 (Deserialization of Untrusted Data),
NIST SSDF PW.7 (Review and analyze human-readable code).
**Detection signal:**
- File: `src/x.py:45`
- Anchor: `data = pickle.loads(user_supplied_input)`
- Pattern: <module>.loads(user_supplied_input)
- Reachability: untrusted, comes from request body
**Proposal:** ...
**Blast radius:** ...
**Reversal plan:** ...
--auto-apply,CRITICAL findings always prompt.
per-batch.
commit and downgrades the finding.
reference are advisory only and skip the apply phase.
--report-only until the user has reviewed at least one
report and explicitly opts into proposals.
The skill composes (rather than re-implements):
pensive:rust-review: full Rust audit when Rust is presentpensive:bug-review: bug-hunting backbonepensive:safety-critical-patterns: NASA Power-of-10 adaptedpensive:tiered-audit: three-tier discipline (--tier 1/2/3)pensive:blast-radius: change-impact assessment for proposalsleyline:supply-chain-advisory: dependency postureleyline:authentication-patterns: auth/credential reviewleyline:content-sanitization: input handlingabstract:hook-authoring: hook-event securityimbue:proof-of-work: evidence discipline for findingsharden:findings-verified)Every finding must cite a real location and a verbatim anchor. Write
findings to .review/findings.json and confirm each citation resolves:
python plugins/imbue/scripts/citation_verifier.py \
--findings .review/findings.json --repo-root .
Drop or label UNVERIFIED any finding the verifier fails (exit 1); only
verified findings enter the report. See Skill(imbue:review-core) Step 5
and Skill(imbue:structured-output) for the schema.
detected in the repo.
report executive summary lists the SSDF practice coverage.
proposal (file, diff or config snippet, blast radius,
reversal plan, expected-passing test).
(or without an --auto-apply flag covering its severity).
gate failure reverted the commit and downgraded the
finding.
reviews/harden-<date>.md exists and lists every findingwith a disposition (applied / filed / deferred / rejected /
advisory).
Location + verbatim Anchorconfirmed by citation_verifier.py (exit 0), or unverified
findings were dropped or labeled UNVERIFIED.
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Take athola/harden from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.