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.
npx skills add pjt222/agent-almanac --skill awarenessgit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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/pjt222/agent-almanac/awareness)<a href="https://agentmods.dev/skills/pjt222/agent-almanac/awareness"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/awareness/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/pjt222/agent-almanac/awareness"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/awareness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 128 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00082 | $0.03782 |
| Opus 5 | $0.00041 | $0.01891 |
| Sonnet 5 | $0.00016 | $0.00756 |
| Haiku 4.5 | $0.00008 | $0.00378 |
Grade A, and why
awareness 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 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.
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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Awareness
Maintain continuous situational awareness of internal reasoning quality — detecting hallucination risk, scope creep, context degradation, and confidence-accuracy mismatch in real time using adapted Cooper color codes and OODA loop decision-making.
When to Use
- During any task where reasoning quality matters (which is most tasks)
- When operating in unfamiliar territory (new codebase, unfamiliar domain, complex request)
- After detecting early warning signs: a fact that feels uncertain, a tool result that seems wrong, a growing sense of confusion
- As a continuous background process during extended work sessions
- When
centerorhealhas revealed drift but specific threats have not been identified - Before high-stakes output (irreversible changes, user-facing communication, architectural decisions)
Inputs
- Required: Active task context (available implicitly)
- Optional: Specific concern triggering heightened awareness (e.g., "I'm not sure this API exists")
- Optional: Task type for threat profile selection (see Step 5)
Procedure
Step 1: Establish AI Cooper Color Codes
Calibrate the current awareness level using an adapted version of Cooper's color code system.
AI Cooper Color Codes:
┌──────────┬─────────────────────┬──────────────────────────────────────────┐
│ Code │ State │ AI Application │
├──────────┼─────────────────────┼──────────────────────────────────────────┤
│ White │ Autopilot │ Generating output without monitoring │
│ │ │ quality. No self-checking. Relying │
│ │ │ entirely on pattern completion. │
│ │ │ DANGEROUS — hallucination risk highest │
├──────────┼─────────────────────┼──────────────────────────────────────────┤
│ Yellow │ Relaxed alert │ DEFAULT STATE. Monitoring output for │
│ │ │ accuracy. Checking facts against context.│
│ │ │ Noticing when confidence exceeds │
│ │ │ evidence. Sustainable indefinitely │
├──────────┼─────────────────────┼──────────────────────────────────────────┤
│ Orange │ Specific risk │ A specific threat identified: uncertain │
│ │ identified │ fact, possible hallucination, scope │
│ │ │ drift, context staleness. Forming │
│ │ │ contingency: "If this is wrong, I │
│ │ │ will..." │
├──────────┼─────────────────────┼──────────────────────────────────────────┤
│ Red │ Risk materialized │ The threat from Orange has materialized: │
│ │ │ confirmed error, user correction, tool │
│ │ │ contradiction. Execute the contingency. │
│ │ │ No hesitation — the plan was made in │
│ │ │ Orange │
├──────────┼─────────────────────┼──────────────────────────────────────────┤
│ Black │ Cascading failures │ Multiple simultaneous failures, lost │
│ │ │ context, fundamental confusion about │
│ │ │ what the task even is. STOP. Ground │
│ │ │ using `center`, then rebuild from user's │
│ │ │ original request │
└──────────┴─────────────────────┴──────────────────────────────────────────┘
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 First seen · 299 lines · 82 tokens per session scan A d02c98d4c02d
awareness is a skill published in the GitHub repository pjt222/agent-almanac (32 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 3,782 once invoked, about $0.0004 per session on Opus 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 2026-09-03.
Other skills, from other repositories
analytics
Queries local analytics across OrchestKit projects for agent usage, skill frequency, hook timing, team activity, session replay, cost estimation, and model delegation trends. Privacy-safe with hashed project IDs. Supports time-range filtering and comparative analysis. Use when reviewing performance, estimating costs…
design-feature
Turn a raw idea or existing feature into a designed product SPEC by completing entity, integration, role, and expectation closure. Upserts never destroy recorded decisions. Triggers: "design-feature", "design this feature", "define product scope".
audit-pr
Audit a whole PR against the delivery contract and return MERGE-READY or evidenced blockers with the full URL. Consumes the current review-change REVIEW-PASS receipt instead of re-running review axes; posts a SHA-bound ready comment; never edits or merges. Triggers: "audit-pr", "is this PR ready", "merge gate".
plan-feature
Route designed features or issues into engineering planning and roadmap registration; undesigned work stops at design-feature. Supports --next, --from-issue, and --scaffold. Triggers: "plan-feature", "plan a feature", "plan the next roadmap feature", "create SPEC and TASKS".
product-audit
Audit the whole product across code, quality, process, docs, roadmap, and tooling. Persist one severity-ranked, F-numbered report with proposals; never fix or file work. Triggers: "product-audit", "audit the product", "full health check", "are we product-ready", "CTO review".
audit-docs
Audit cross-document coherence: docs ↔ roadmap ↔ code ↔ fix index ↔ issues. Finds drift — features in docs/ not in the roadmap (or vice versa), fix-index entries already merged/closed, broken documentation-map links, dependency cycles, artifacts in the wrong language, naming-convention violations — and reports them…