learn
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npx mdskill add H-mmer/pentest-agents/learnRecord platform response: $ARGUMENTS
SKILL.md
.github/skills/learnView on GitHub ↗
--- name: learn description: "Record a platform response and update learning. Usage: /learn <report_id> <status> [--bounty 500] [--vuln-type XSS]" disable-model-invocation: false --- Record platform response: $ARGUMENTS 1. Parse arguments and run: `uv run python3 $CLAUDE_PROJECT_DIR/tools/response_tracker.py log $ARGUMENTS` 2. Also update brain: `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py log "Report response: $ARGUMENTS"` 3. Sync to global brain: `uv run python3 $CLAUDE_PROJECT_DIR/tools/global_brain.py sync-from-local` 4. Show updated insights: `uv run python3 $CLAUDE_PROJECT_DIR/tools/response_tracker.py insights` ## Top-Tier Learning Loop Convert every platform response into a future hunting rule. - If accepted: record the decisive proof artifact, impact framing, asset type, vuln variant, bounty tier, and why triage agreed. - If duplicate: record the duplicated primitive and which uniqueness signal was missing. - If N/A: record the exact sentence or policy clause that killed it. - If informative: record the missing chain or business impact required to make it payable. - If severity changed: record the evidence that moved it up or down. End with one concrete update: a brain pattern, a never-submit rule, a report wording change, or a target ranking adjustment.