m3taversal
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Wrote sourced_from: into 414 claim files pointing back to their origin source. Backfilled claims_extracted: into 252 source files that were processed but missing this field. Matching uses author+title overlap against claim source: field, validated against 296 known-good pairs from existing claims_extracted. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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| type | title | author | url | date_published | date_archived | domain | status | processed_by | tags | sourced_via | claims_extracted | |||||
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| source | 25 Prompts for Making AI Agents Self-Diagnose | kloss (@kloss_xyz) | https://x.com/kloss_xyz/status/2032223154094162063 | 2026-03-09 | 2026-03-16 | ai-alignment | processed | theseus |
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Leo routed from X ingestion (@kloss_xyz tweet 2032223154094162063) |
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25 Prompts for Making AI Agents Self-Diagnose
Practitioner-generated prompt collection for inducing metacognitive monitoring in AI agents. Published as a tweet thread by @kloss_xyz.
Prompt Categories (my analysis)
Uncertainty calibration (5): #4 confidence rating, #5 missing information, #15 evidence quality, #16 deductive vs speculative, #23 likely→certain threshold
Failure mode anticipation (4): #1 biggest failure risk, #6 what wrong looks like, #11 three most likely failure modes, #19 what context invalidates approach
Tool/output verification (3): #2 schema verification, #7 expected tool return, #8 actual vs expected comparison
Strategy meta-monitoring (4): #9 step count check, #13 redo from scratch, #18 solving right problem, #20 loop detection
Adversarial self-review (3): #12 argue against answer, #14 expert critique, #17 simplest explanation (Occam's)
User alignment (3): #10 unstated user intent, #21 define done, #25 optimize for user's use case
Epistemic discipline (3): #22 replace "I think" with evidence, #24 simpler solution check, #3 flag uncertainty explicitly
Evidence Base
No empirical validation of these prompts. This is practitioner knowledge, not a study. However, connects to validated finding that structured prompting produces measurable performance gains (Residue prompt reduced human intervention 6x — Reitbauer 2026).
Extraction Status
- 1 claim: structured self-diagnosis prompting as oversight scaffolding