/assess-obstacle-severity
Rate each identified obstacle's difficulty — overcomability, time cost,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill assess-obstacle-severity --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/assess-obstacle-severity
Context preview
The summary Claude sees to decide when to auto-load this skill.
Rate each identified obstacle's difficulty — overcomability, time cost,
SKILL.md
assess-obstacle-severity.SKILL.mdname: assess-obstacle-severity description: Rate each identified obstacle's difficulty — overcomability, time cost, workaround existence. May optionally use search tools to validate assessments. execution: subagent prompt: ./prompt.md input: obstacles (string), actor_profile (string) dependencies: sops: - spawn-agent
Assess Obstacle Severity
Assess how severe each obstacle actually is for this specific user.
Execution
Subagent — spawned via `subagent-spawning/spawn-agent` skill.
Input
- Identified obstacles list
- ActorProfile
Search
Optional — may use web-search, web-research, literature-overview, literature-search, literature-research to validate assessments.
Output
Each obstacle rated on:
- Overcomability: 1-week learnable / 1-month effort / 6-month investment / fundamental blocker
- Time cost estimate
- Workaround existence (yes/no + description)
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Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill