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dsh-evidence-ledger

Memory Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install Freakz2z/dsh-evidence-ledger

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install Freakz2z/dsh-evidence-ledger from your DeepSeek Harness workspace root to install this plugin; the source is available at https://github.com/Freakz2z/dsh-evidence-ledger .

About this plugin

In multi-step DSH agent sessions, observed test results, decisions made, and failure paths tend to vanish once context is trimmed or the conversation resets. Without a structured record, debugging and reproducibility become nearly impossible. dsh-evidence-ledger pins these critical facts into an append-only JSONL file inside the workspace, creating a local evidence ledger that never phones home and never deletes prior entries.

The evidence_ledger tool classifies entries by kind (fact, test, decision, failure, note) and status (observed, verified, rejected, pending). list applies exact filters on kind, status, and tag; query performs case-insensitive full-text search across claims, evidence, sources, and tags; summary returns total matches, aggregated counts by kind and status, and the most recent matching record. By default the ledger lives at .dsh/evidence-ledger.jsonl, with paths restricted to workspace-relative locations, rejecting absolute and traversal paths.

This plugin is well suited to developers running multi-step reasoning, automated testing, or code-review workflows where a traceable evidence trail matters. It does not judge whether a claim is true; it simply preserves raw records and provides searchable source pointers, so every conclusion can be audited later.

Use Cases

  • Preserving test results and decisions across multi-step reasoning
  • Tracing failure paths and verification status during code review
  • Retaining critical facts that would be lost to context trimming in long sessions

Best For

  • Developers running automated tests with DSH agents
  • Research or engineering teams that need an auditable evidence trail
  • Architects building multi-step workflows where traceability matters