Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add haabe/mycelium/plugin install myceliumWrote 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/skills/haabe/mycelium/diamond-assess)<a href="https://agentmods.dev/skills/haabe/mycelium/diamond-assess"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/diamond-assess/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/skills/haabe/mycelium/diamond-assess"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/diamond-assess.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 | $0.00026 | $0.03955 |
| Opus 5 | $0.00013 | $0.01978 |
| Sonnet 5 | $0.00005 | $0.00791 |
| Haiku 4.5 | $0.00003 | $0.00396 |
Grade B, and why
diamond-assess scanned grade B with 1 finding 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 5d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
- For each `status: active` source, find the newest file in `.claude/evals/metrics/<source>/`. How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diamond Assess Skill
Evaluate current diamond state and recommend next action.
Preflight: Read-before-Recommend (gate-narration discipline)
Hard rule (per CLAUDE.md Communication Rules, anti-pattern #7 graduation v0.39.16). Every gate-status narration, blocker statement, hold claim, or "what's missing" verdict this skill emits MUST cite the canvas file + field path of the source evidence (e.g., per purpose.yml#why, per opportunities.yml#opp-005#status, per landscape.yml:1520). Adjacent-surface inference (different opportunity, different ht, different topic) MUST be tagged as inference, not asserted as gate state. This skill ran an un-mechanized version of its own diagnosis in cluster-instances.md instance #17 (2026-06-02) — confabulated an "L0 unclear" blocker from comms-friction evidence while the L0 purpose was clear and canvas-documented; collapsed only after the founder articulated the underlying model and grep verified the canvas already had it. The preamble exists so this skill stops being the recursive case.
Workflow
-
Cognitive Forcing (ALWAYS FIRST — before any analysis):
Before presenting any assessment, ask the human for their unprimed judgment:
"Before I run the gates — where do you think this diamond stands right now? What feels solid and what feels shaky?"
Wait for the human's response. Record it. Then proceed with the full assessment below. After presenting the assessment (step 10), compare:
"You said [X]. The gates say [Y]. Where do we differ?"
This prevents the agent's analysis from anchoring the human's judgment. The human's pre-assessment often catches things the gates miss (Hoskins consistently outperformed the agent on product judgment calls).
Source: Buçinca, Malaya & Gajos (Cognitive Forcing Functions, Harvard CHI/CSCW 2021) — forcing initial human judgment before AI output significantly reduces automation bias and over-reliance on incorrect AI recommendations.
Autonomous mode (per
${CLAUDE_PLUGIN_ROOT}/engine/autonomous-mode.md): in a declared autonomous run, substitute at rung (b) — record the declared persona's unprimed judgment BEFORE reading any canvas or gate state (the ordering is the load-bearing part, not the human authorship), tag itsource_class: internal_simulated, and ledger the substitution. The post-assessment comparison still runs: persona judgment vs gate verdict.
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.
- 5d ago Changed · +4 lines 34febf0025b5
- 9d ago First seen · 216 lines · 26 tokens per session scan B a2a0fd526e08
diamond-assess is a skill published in the GitHub repository haabe/mycelium (45 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 3,955 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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