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feat: consolidate all Anthropic plugins into ECC v2.0.0

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Ports functionality from 10+ separate plugins into ECC so users only
need one plugin installed. Consolidates: pr-review-toolkit, feature-dev,
commit-commands, hookify, code-simplifier, security-guidance,
frontend-design, explanatory-output-style, and personal skills.

New agents (8): code-architect, code-explorer, code-simplifier,
comment-analyzer, conversation-analyzer, pr-test-analyzer,
silent-failure-hunter, type-design-analyzer

New commands (9): commit, commit-push-pr, clean-gone, review-pr,
feature-dev, hookify, hookify-list, hookify-configure, hookify-help

New skills (8): frontend-design, hookify-rules, github-ops,
knowledge-ops, lead-intelligence, oura-health, pmx-guidelines, remotion

Enhanced skills (8): article-writing, content-engine, market-research,
investor-materials, investor-outreach, x-api, security-scan,
autonomous-loops — merged with personal skill content

New hook: security-reminder.py (pattern-based OWASP vulnerability
warnings on file edits)

Totals: 36 agents, 69 commands, 128 skills, 29 hook scripts
This commit is contained in:
Affaan Mustafa
2026-03-31 21:54:03 -07:00
parent 19755f6c52
commit 4813ed753f
73 changed files with 5618 additions and 27 deletions
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32
skills/hermes-generated/research-ops/SKILL.md
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@@ -1,6 +1,6 @@
---
name: research-ops
description: Evidence-first research workflow for Hermes. Use when answering current questions, evaluating a market or tool, enriching leads, or deciding whether a request should become ongoing monitored data collection.
description: Evidence-first research workflow for Hermes. Use when answering current questions, evaluating a market or tool, enriching leads, comparing strategic options, or deciding whether a request should become ongoing monitored data collection.
metadata:
hermes:
tags: [generated, research, market, discovery, monitoring, workflow, verification]
@@ -16,6 +16,7 @@ Pull these imported skills into the workflow when relevant:
- `deep-research` for multi-source cited synthesis
- `market-research` for decision-oriented framing
- `exa-search` for first-pass discovery and current-web retrieval
- `continuous-agent-loop` when the task spans user-provided evidence, fresh verification, and a final recommendation across multiple turns
- `data-scraper-agent` when the user really needs recurring collection or monitoring
- `search-first` before building new scraping or enrichment logic
- `eval-harness` mindset for claim quality, freshness, and explicit uncertainty
@@ -24,28 +25,41 @@ Pull these imported skills into the workflow when relevant:
- user says `research`, `look up`, `find`, `who should i talk to`, `what's the latest`, or similar
- the answer depends on current public information, external sources, or a ranked set of candidates
- the user pastes a compaction summary, copied research, manual calculations, or says `factor this in`
- the user asks `should i do X or Y`, `compare these options`, or wants an explicit recommendation under uncertainty
- the task sounds recurring enough that a scraper or scheduled monitor may be better than a one-off search
## Workflow
1. Classify the ask before searching:
1. Start from the evidence already in the prompt:
- treat compaction summaries, pasted research, copied calculations, and quoted assumptions as loaded inputs
- normalize them into `user-provided evidence`, `needs verification`, and `open questions`
- do not restart the analysis from zero if the user already gave you a partial model
2. Classify the ask before searching:
- quick factual answer
- decision memo or comparison
- lead list or enrichment
- recurring monitoring request
2. Start with the fastest evidence path:
3. Build the decision surface before broad searching when the ask is comparative:
- list the options, decision criteria, constraints, and assumptions explicitly
- keep concrete numbers and dates attached to the option they belong to
- mark which variables are already evidenced and which still need outside verification
4. Start with the fastest evidence path:
- use `exa-search` first for broad current-web discovery
- if the question is about a local wrapper, config, or checked-in code path, inspect the live local source before making any web claim
3. Deepen only where the evidence justifies it:
5. Deepen only where the evidence justifies it:
- use `deep-research` when the user needs synthesis, citations, or multiple angles
- use `market-research` when the result should end in a recommendation, ranking, or go/no-go call
4. Separate fact from inference:
- keep `continuous-agent-loop` discipline when the task spans user evidence, fresh searches, and recommendation updates across interruptions
6. Separate fact from inference:
- label sourced facts clearly
- label user-provided evidence clearly
- label inferred fit, ranking, or recommendation as inference
- include dates when freshness matters
5. Decide whether this should stay manual:
7. Decide whether this should stay manual:
- if the user will likely ask for the same scan repeatedly, use `data-scraper-agent` patterns or propose a monitored collection path instead of repeating the same manual research forever
6. Report with evidence:
8. Report with evidence:
- group the answer into sourced facts, user-provided evidence, inference, and recommendation when the ask is a comparison or decision
- cite the source or local file behind each important claim
- if evidence is thin or conflicting, say so directly
@@ -53,12 +67,16 @@ Pull these imported skills into the workflow when relevant:
- do not answer current questions from stale memory when a fresh search is cheap
- do not conflate local code-backed behavior with market or web evidence
- do not ignore pasted research or compaction context and redo the whole investigation from scratch
- do not mix user-provided assumptions into sourced facts without labeling them
- do not present unsourced numbers or rankings as facts
- do not spin up a heavy deep-research pass for a quick capability check that local code can answer
- do not leave the comparison criteria implicit when the user asked for a recommendation
- do not keep one-off researching a repeated monitoring ask when automation is the better fit
## Verification
- important claims have a source, file path, or explicit inference label
- user-provided evidence is surfaced as a distinct layer when it materially affects the answer
- freshness-sensitive answers include concrete dates when relevant
- recurring-monitoring recommendations state whether the task should remain manual or graduate to a scraper/workflow