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 agentmods add skills/tupe12334/instinct/feedback-analysisnpx skills add tupe12334/instinct --skill feedback-analysisgit clone --depth 1 https://github.com/tupe12334/instinctWhat 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 | $0.00018 | $0.01838 |
| Opus 5 | $0.00009 | $0.00919 |
| Sonnet 5 | $0.00004 | $0.00368 |
| Haiku 4.5 | $0.00002 | $0.00184 |
Grade A, and why
feedback-analysis 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 2d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feedback Analysis
Overview
Method developed by Peter Drucker. Before taking action or making a decision, write down what you expect to happen. Then, 9–12 months later, compare actual results to expectations. Repeated over time, this surfaces your actual strengths, weaknesses, and blind spots — not your assumed ones.
Core insight: most people don't know what they're actually good at. Systematic feedback analysis reveals it objectively.
How to Apply
Step 1 — Record expectations (before action)
Whenever you make a significant decision or take a key action, write:
- Date: [today]
- Decision/Action: [what you're doing]
- Expected outcome: [what you predict will happen, and by when]
- Key assumptions: [what must be true for your prediction to hold]
- Success criteria: [how you'll know the outcome was what you expected]
Store this somewhere reviewable (notes app, journal, database).
Step 2 — Review (9–12 months later)
Retrieve your original record and compare:
- Actual outcome: [what happened]
- Prediction accuracy: [on track / better than expected / worse than expected]
- Variance explanation: [why did reality differ from prediction?]
Step 3 — Pattern analysis (after multiple cycles)
After 6+ feedback cycles, look for patterns:
Identify strengths: Areas where your predictions consistently match or beat reality. These are your actual competencies — lean into them.
Identify weaknesses: Areas where results consistently disappoint expectations. Either improve these skills or stop relying on them.
Identify blind spots: Areas where you were confidently wrong. These require the most attention — wrong confidence is more dangerous than acknowledged uncertainty.
Identify waste: Tasks or areas where you invest significant time but results are mediocre. Consider stopping or delegating.
Step 4 — Act on findings
- Double down on strengths: Allocate more time and resources here; this is where you create the most value
- Remediate critical weaknesses: If a weakness blocks your goals, address it; otherwise, work around it
- Fix or avoid blind spots: Either acquire feedback sooner next time, or stop operating in areas where your judgment is systematically unreliable
- Eliminate wasted effort: Stop doing things you're not good at and don't need to be good at
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.
- 2d ago First seen · 139 lines · 18 tokens per session scan A 8579e582cbd3
feedback-analysis is a skill published in the GitHub repository tupe12334/instinct (1 stars, last pushed 16d ago), licensed MIT. It adds 18 tokens to every session and 1,838 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
thinking-model-router
When unsure which thinking skill fits, map domain and problem type, then return NONE or one primary skill by default (at most three complementary).
thinking-red-team
For authorized security review of code, auth, or APIs you control, model the attacker, map the attack surface, and report only findings with a reproducible exploit path and verified mitigation.
thinking-scientific-method
When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
thinking-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
thinking-circle-of-competence
Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.