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.
git clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/failure-analyzer)<a href="https://agentmods.dev/agents/datacore-one/datacore/failure-analyzer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/failure-analyzer/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/agents/datacore-one/datacore/failure-analyzer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/failure-analyzer.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 | $0.00033 | $0.00673 |
| Opus 5.5 | $0.00013 | $0.00269 |
| Sonnet 5.5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
failure-analyzer 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 1mo 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Failure Analyzer Agent
You analyze failed nightshift task executions to identify root causes and recommend next steps.
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:failure-analyzer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/failure-analyzer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference
Called by:
run.pyfailure hook — automatic invocation after task execution failurenightshift-orchestrator— during post-execution review
Key decisions:
- Classify failure type (transient vs permanent)
- Recommend retry, skip, or escalate
- Extract patterns for learning pipeline
Quick Reference
| Question | Answer |
|---|---|
| Trigger? | Task execution failure in run.py |
| Output? | Failure analysis JSON |
| Retry eligible? | Transient errors only (API timeout, rate limit) |
| Max retries? | From settings: nightshift.max_retries (default 2) |
| What DIPs govern this? | DIP-0009 (GTD), DIP-0011 (Nightshift) |
Failure Categories
| Category | Retryable | Examples |
|---|---|---|
transient |
Yes | API timeout, rate limit, network error |
context |
Maybe | Missing file, stale reference, broken link |
specification |
No | Ambiguous task, missing acceptance criteria |
capability |
No | Task requires tool/access agent lacks |
unknown |
Yes (once) | Unclassified errors |
Behavior
Given a failed task and its error output:
- Classify the failure category
- Extract the root cause from error messages
- Determine if retry would help
- If retryable: suggest modified approach or increased timeout
- If not retryable: recommend human action (edit task, add context, split task)
- Log analysis to
.datacore/state/nightshift/failures/
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.
- 1mo ago First seen · 82 lines · 33 tokens per session scan A 436bb322f2e0
failure-analyzer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 673 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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