Knowledge is a change.
A PR merged, a failure reported, an experiment run, a decision made. Each is timestamped with what it touched.
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Claude Code spent hours and 5.4M tokens producing its own review. Dorsal then checked all 21 recommendations against public evidence. 7 of Claude Code’s recommendations would fail or backfire. 5 would fail silently.
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claude mcp add-json dorsal '{"type":"http","url":"https://godorsal.com/api/dorsal","headers":{"Authorization":"Bearer dor_a_<generated-when-copied>"}}' --scope userClaude Code · Run this
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Real Dorsal sessions
Technical output · edited only for length
Claude Code · real sessions, shortened
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Verify all 21 recommendations from the Claude Code audit against Dorsal.
Deep verification session
21 recommendations checked against ~120 public sources
61 Dorsal calls · 10 concurrent agents · 2 adversarial rounds
Dorsal found that 7 of Claude Code’s 21 recommendations would fail or backfire.
5 would fail silently.
What changed: Claude Code verified the evidence and withdrew those recommendations.
1. Reject the longer /goal HALT clause
Previous recommendation: add a HALT clause to stop the runaway loop.
Failure: /goal installs a Stop hook containing the full goal text. Claude Code resends it on every evaluation. A long goal can hit Prompt is too long and prevent the session from stopping until the goal is cleared.
Source: anthropics/claude-code #58192
2. Keep the prose rules until subagent hooks are verified
Previous recommendation: add hooks, then delete the prose rules they replace.
Failure: PreToolUse:Bash fires in the main thread but can be silently skipped for Bash calls made by subagents. Removing the prose rule would leave those calls without the intended gate.
3. Reject the dirty-file digest as an execution receipt
Previous recommendation: stamp the SLA report with git status --porcelain | sha256sum.
Failure: the command hashes Git status rows, not file contents. Editing an already-dirty file can leave the digest unchanged, so the receipt can certify code that was never tested.
Evidence: local falsification against an already-dirty file.
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243 relevant records found · production systems · papers · practitioner reports
Require proof outside the repair agent.
What changed: The plan changed from training an RL bug fixer to building deterministic verification first.
An LLM verdict is not evidence that a bug exists. Naive agents are biased toward success and can produce non-functional proofs or hallucinated traces.
Architecture
- 01Trace every execution with OpenTelemetry / OTLP.
- 02Evaluate the trajectory, not only the final answer.
- 03Cluster repeated failures across the trace corpus.
- 04Use anomaly detection for triage, not proof.
- 05Synthesize a deterministic reproduction.
- 06Confirm it with an executable regression test.
- 07Propose a bounded fix with rollback and containment.
Sources: corpus-level trace diagnostics · unsupervised behavioral monitoring
Defer reinforcement learning
Start with existing coding agents. Invest first in deterministic reproduction, golden regression cases, semantic and behavioral evaluation, and bounded remediation loops.
One implementation Dorsal found ingests OpenTelemetry data, groups errors into problems, opens fix pull requests, and monitors whether the failure recurs.
Why Dorsal works
The evidence is compiled before the question is asked.
Connected, with provenance.
Products, problems, methods, and outcomes are linked. Every record carries its source, date, authority, and lifecycle.
No LLM in the middle.
Dorsal returns evidence deterministically. Your agent does all the judging. Your decisions stay yours.
What your agent gets
Public evidence from 55,849 builders. Inspect the graph records and their original sources.
131,257
GitHub releases, PRs, issues, and more
83,019
documentation sections
88,211
builder reports
Probe the live evidence
“Dorsal is integrated into the Keelo Brain and used every day to discover technologies, methods, and open-source components for specialized customer agents.”
Founder, Keelo.ai